Relevant bibliographies by topics / Brain tumour

Academic literature on the topic 'Brain tumour'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Brain tumour"

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Watson, Shaun. "Brain tumour." Lancet 359, no. 9301 (January 2002): 177. http://dx.doi.org/10.1016/s0140-6736(02)07388-9.

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Srivastava, Smriti. "Brain Tumor Prediction Using Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1513–17. http://dx.doi.org/10.22214/ijraset.2021.36616.

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Brain Tumor is a disease in which there is an abnormal growth of mass that occurs inside human brain that can led to death also. The detection of brain tumor takes places through MRI scan images. For doctors sometimes it becomes difficult to differentiate between tumour cells and nerve cells. Even sometimes what happens is that unstructured shape of tumours led it make difficult for doctors to identify tumours in brain. Artificial intelligence is one of the most trending technologies now a day through which machines gets the power to think and take decisions on its own. This paper uses the power of Artificial Intelligence to detect Brain tumour in human brain.
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Malarvizhi, A. B., A. Mofika, M. Monapreetha, and A. M. Arunnagiri. "Brain tumour classification using machine learning algorithm." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012042. http://dx.doi.org/10.1088/1742-6596/2318/1/012042.

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Abstract A Brain tumour is formed by a gradual addition of abnormal cells, and this is one of the major causes of death among other sorts of cancers. It is necessary to classify brain tumor using Magnetic Resonance Imaging (MRI) brain tumor image for treatment because MRI images assist as to detect the smallest defect of the body. This paper aimed to automatically classify brain tumours using a machine learning algorithm. In this work, the input image of the brain was pre-processed using median filter, segmented from the background using thresholding and K-means clustering algorithm and its features were extracted using GLCM. Using the SVM classifier, the brain tumour in the image was detected as either benign or malignant. This image classification process helps the doctors and research scientists to detect the tumour during its early stages, thereby controlling the spread of cancerous cells.
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Dirks, Peter B. "Brain tumour stem cells: the undercurrents of human brain cancer and their relationship to neural stem cells." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1489 (February 19, 2007): 139–52. http://dx.doi.org/10.1098/rstb.2006.2017.

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Conceptual and technical advances in neural stem cell biology are being applied to the study of human brain tumours. These studies suggest that human brain tumours are organized as a hierarchy and are maintained by a small number of tumour cells that have stem cell properties. Most of the bulk population of human brain tumours comprise cells that have lost the ability to initiate and maintain tumour growth. Although the cell of origin for human brain tumours is uncertain, recent evidence points towards the brain's known proliferative zones. The identification of brain tumour stem cells has important implications for understanding brain tumour biology and these cells may be critical cellular targets for curative therapy.
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Ahlbom, Anders, Ylva Rodvall, Y. Ben-Shlomo, and G. Davey Smith. "BRAIN TUMOUR TRENDS." Lancet 334, no. 8674 (November 1989): 1272–73. http://dx.doi.org/10.1016/s0140-6736(89)91873-4.

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Shivdikar, Adish, Mihir Shirke, Ishwar Vodnala, and Prof Jaychand Upadhaya. "Brain Tumor Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 621–27. http://dx.doi.org/10.22214/ijraset.2022.40710.

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Abstract: Tumors are now the second major cause of cancer. A huge percentage of patients are in danger as more than just a consequence of cancer. The medical field needs fast, automated, efficient and reliable technique to detect tumor like brain tumor. Detection plays very important role in treatment. If proper detection of tumor is possible then doctors keep a patient out of danger. Various image processing techniques are used in this application. Doctors are able to provide excellent treatment and save a huge number of tumour patients by using this application. A tumour is nothing more than an uncontrolled growth of cells. Brain tumour cells expand to the point where they consume all of the nutrition intended for healthy cells and tissues, resulting in brain failure. Currently, doctors manually locate the position and area of a brain tumour by looking at the patient's MR images of the brain. This leads to inaccuracy in tumour detection and is extremely time intensive. A tumour is an uncontrollably growing clump of tissue. We can utilise CNN (Convolution Neural Network), also known as NN (Neural Network), and VGG 16 Deep Learning architectures (visual geometry group). To diagnose a brain tumour, transfer learning is used. The model's performance predicts whether or not a tumour is present in an image. If a tumour is present, the answer is yes; otherwise, the answer is no. Keywords: Brain Tumor, MRI, OpenCV, Data Augmentation, CNN, Transfer learning, VGG.
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S, Manimurugan. "Classification of Alzheimer's disease from MRI Images using CNN based Pre-trained VGG-19 Model." Journal of Computational Science and Intelligent Technologies 1, no. 2 (2020): 34–41. http://dx.doi.org/10.53409/mnaa.jcsit20201205.

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Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vectormachine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.
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T, Anitha, Charlyn Pushpa Latha G, and Surendra Prasad M. "A Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO." Journal of Computational Science and Intelligent Technologies 1, no. 3 (2020): 9–14. http://dx.doi.org/10.53409/mnaa.jcsit20201302.

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Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.
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Nonaka, Motohiro, Misa Suzuki-Anekoji, Jun Nakayama, Hideaki Mabashi-Asazuma, Donald L. Jarvis, Jiunn-Chern Yeh, Kazuhiko Yamasaki, et al. "Overcoming the blood–brain barrier by Annexin A1-binding peptide to target brain tumours." British Journal of Cancer 123, no. 11 (September 14, 2020): 1633–43. http://dx.doi.org/10.1038/s41416-020-01066-2.

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Abstract Background Annexin A1 is expressed specifically on the tumour vasculature surface. Intravenously injected IF7 targets tumour vasculature via annexin A1. We tested the hypothesis that IF7 overcomes the blood–brain barrier and that the intravenously injected IF7C(RR)-SN38 eradicates brain tumours in the mouse. Methods (1) A dual-tumour model was generated by inoculating luciferase-expressing melanoma B16 cell line, B16-Luc, into the brain and under the skin of syngeneic C57BL/6 mice. IF7C(RR)-SN38 was injected intravenously daily at 7.0 μmoles/kg and growth of tumours was assessed by chemiluminescence using an IVIS imager. A similar dual-tumour model was generated with the C6-Luc line in immunocompromised SCID mice. (2) IF7C(RR)-SN38 formulated with 10% Solutol HS15 was injected intravenously daily at 2.5 μmoles/kg into two brain tumour mouse models: B16-Luc cells in C57BL/6 mice, and C6-Luc cells in nude mice. Results (1) Daily IF7C(RR)-SN38 injection suppressed tumour growth regardless of cell lines or mouse strains. (2) Daily injection of Solutol-formulated IF7C(RR)-SN38 led into complete disappearance of B16-Luc brain tumour in C57BL/6 mice, whereas this did not occur in C6-Luc in nude mice. Conclusions IF7C(RR)-SN38 crosses the blood–brain barrier and suppresses growth of brain tumours in mouse models. Solutol HS15-formulated IF7C(RR)-SN38 may have promoted an antitumour immune response.
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Muller, Paul J., and Brian C. Wilson. "Photodynamic Therapy of Malignant Brain Tumours." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 17, no. 2 (May 1990): 193–98. http://dx.doi.org/10.1017/s0317167100030444.

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ABSTRACT:Fifty patients with malignant supratentorial tumours were treated with intra-operative photodynamic therapy (PDT); in 33 cases the tumour was recurrent. In 45 patients the tumour was a cerebral glioma and in 5 cases a solitary cerebral metastasis. All patients received a porphyrin photosensitizer 18-24 hours pre-operatively. Photoillumination was carried out at 630 nm to a tumour cavity created by radical tumour resection and/or tumour cyst drainage. The light energy density ranged from 8 to 175 J/cm2. In 8 patients additional interstitial light was administered. The operative mortality was 4%. Follow up has ranged from 1 to 30 months. The median survival for the 45 primary malignant tumours was 8.6 months with a 1 and 2 year actuarial survival rate of 32% and 18%, respectively. In 12 patients a complete or near complete CT scan response was identified post PDT. These patients tended to have a tumour geometry (eg. cystic) that allowed complete or near complete light distribution to the tumour. The median survival for this group was 17.1 months with a 1 and 2 year actuarial survival of 62% and 38%, respectively. In the 33 cases who did not have a complete response the median survival was 6.5 months with a 1 and 2 year actuarial survival of 22% and 11%, respectively. Photodynamic therapy of malignant brain tumours can be carried out with acceptable risk. Good responses appear to be related to adequate light delivery to the tumour.
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Dissertations / Theses on the topic "Brain tumour"

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Cubis, Lee M. "Staying Connected after Brain Tumour: Changes in Social Networks and Relationship to Wellbeing after Brain Tumour." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/395565.

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People with primary brain tumour typically experience high levels of functional impairment with an uncertain and often poor prognosis. Outcome studies highlight that up to 48% of people with brain tumour experience clinical levels of psychological distress that can persist throughout the course of living with the illness. Strong social ties can protect against the adverse effects of chronic illness on psychological wellbeing; however, the functional impairments associated with brain tumour can affect relationships and reduce social participation. There is currently poor understanding of the impact of primary brain tumour on social participation, and how social networks influence wellbeing for this population. Comprising four studies, the broad objective of this thesis was to advance understanding of the impact of brain tumour on social groups and the implications for psychological wellbeing. Study 1 employed a cross-sectional design to investigate how social group memberships influence the relationship between subjective functional impairment and psychological wellbeing. Seventy adults (60% female; aged 22-75 years) with primary brain tumour (46% benign; 18% low-grade; 36% high-grade) undertook a telephone-based assessment including self-report measures of cognitive and physical impairment, social group memberships, confidence in support, and psychological wellbeing. Higher functional impairment was associated with a loss of pre-existing social groups which, in turn, was associated with greater depression and anxiety symptoms. Further, confidence in social groups moderated the relationship between functional impairment and psychological wellbeing. Specifically, those perceiving high levels of functional impairment who had high confidence in their social groups reported lower depression and anxiety than those who had low confidence in their social groups. Study 2 involved a meta-synthesis that aimed to identify, appraise and integrate the findings of qualitative studies that reveal the impact of brain tumour on social networks. The findings of 21 eligible studies were synthesised to reveal three core themes depicting the social trajectory of living with brain tumour: 1) Life disrupted; 2) Navigating the new reality of life; and 3) Social survivorship versus separation. The findings of the meta-synthesis highlighted that changes to social participation commonly occurred throughout the illness including a loss of pre-existing social networks and the emergence of new ones. This review identified a need for further qualitative research to understand how brain tumour affects peoples’ ability to maintain and/or rebuild social networks, and how social networks influence psychological wellbeing. Studies 3 and 4 employed a phenomenological qualitative methodology to explore the lived experience of the social and psychological impacts of brain tumour. Twenty participants (65% female; aged 22-69 years) were purposively selected from the Study 1 sample (N = 70) according to the following characteristics: type and grade of brain tumour (i.e., benign, low grade glioma, malignant/high grade glioma); sex; age (<40, 40-55, >55 years) and time since diagnosis (≤ 24 months or >24 months). Two interviews, three months apart, comprised a Social Identity Mapping exercise (Cruwys et al., 2016) and semi-structured interview exploring changes in social groups and the impact of social groups on psychological wellbeing. Study 3 aimed to understand how brain tumour influences peoples’ ability to manage, maintain, and rebuild their social networks. The main research questions were: 1) how does brain tumour affect people’s ability to stay connected to their social groups? and 2) how do they manage to stay connected or build new connections despite their functional impairments? Two overarching and interrelated themes emerged: engaging and connecting and then versus now. Many individuals experienced significant barriers to social participation including functional impairments, fear of negative evaluation and attributions about the discomfort of others. The ability to harness facilitators and/or develop strategies to overcome these barriers influenced whether individuals experienced stability, maintenance and expansion, loss and rebuilding, or loss and shrinkage of their social networks over time. Study 4 aimed to explore the meaning and functions of social groups, and how these influence wellbeing after brain tumour. The main research questions were: 1) what are the meaning and functions of social groups after brain tumour? and 2) how do individuals perceive that social groups influence their wellbeing? Two major themes emerged relating to the ways in which social groups contribute to and/or detract from wellbeing. The first theme, feeling understood and fulfilled by social groups, depicted perceptions of social groups as positively contributing to wellbeing and sense of identity. The second theme, feeling disappointed in, devalued or drained by social groups, highlighted ways in which social groups detract from wellbeing. Overall, this thesis advances understanding of how primary brain tumour affects people’s ability to stay connected to their social networks and how social groups influence wellbeing. Specifically, Study 1 highlighted both mediating and moderating pathways through which social groups can influence the relationship between functional impairments and psychological wellbeing. Study 2 yielded a novel framework depicting the changes in social roles and relationships across the trajectory of living with brain tumour. Study 3 highlighted that an interplay of barriers, facilitators and strategies influence people’s ability to manage, maintain and/or rebuild social networks after brain tumour. Study 4 revealed that social networks can both contribute to and detract from psychological wellbeing after brain tumour. Collectively, these findings highlight the need for supportive care interventions to focus on enhancing social participation as an avenue for improving psychological wellbeing after brain tumour.
Thesis (Professional Doctorate)
Doctor of Philosophy in Clinical Psychology (PhD ClinPsych)
School of Applied Psychology
Griffith Health
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Zumel, Marne María Ángela 1984. "Environmental factors and brain tumour risk in young people." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/668182.

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Risk factors and diagnosis in young people have been little explored, despite brain tumours (BT) is one of the most frequent tumour type in children and adolescents. The purpose of this doctoral thesis is to study 1) the clinical characteristics and symptoms of BTs in young people, based on the international MOBI-Kids case-control study; 2) a systematic review (SR) of the literature on risk of BTs in young people in relation to environmental factors; 3) the BT risk in relation to chemicals present in drinking water and to heavy metals. The analyses of clinical characteristics revealed that the vast majority of tumours were neuroepithelial (mostly gliomas), followed by embryonal tumours and meningiomas. Overall, the most frequent symptoms were headache, followed by focal neurological signs and symptoms, nausea/ vomiting and visual signs and symptoms, being a 4% of the cases asymptomatic. The average time of diagnosis tended to be short (median 1.42 months), though this varied according to tumour type, age and type of symptom. I found many studies that showed an association between environmental factors (including tobacco smoke, pesticides and diet, among other exposures) and BT risk in the SR. Because of methodological limitations however, the evidence about the role of these factors in the aetiology of this disease is still uncertain. Our analyses in relation to water chemicals showed ORs below 1 for exposures to THMs, and ORs above 1 for nitrate exposure, for both pre- and postnatal exposure periods, some statistically significant so. Our analyses of heavy metals showed ORs below 1for exposures to chromium. However, literature is scarce about this association. Overall, this thesis served to fill a gap in knowledge concerning 1) the clinical characteristics of BT in young people, useful to both clinical practice and aetiological research; 2) causes of this disease; 3) the role of heavy metals and ubiquitous chemicals in water. Further research needs on the aetiology and prevention of BTs in young people are provided.
Los factores de riesgo y el diagnóstico en los jóvenes han sido poco explorados, a pesar de que los tumores cerebrales (TC) son uno de los tipos de tumores más frecuentes en los niños y jóvenes. El propósito de esta tesis doctoral es el estudio de 1) de las características clínicas y los síntomas de los TC en los jóvenes, basados en el estudio internacional de casos y controles MOBI-Kids; 2) una revisión sistemática de la literatura sobre el riesgo de TC en jóvenes en relación con factores ambientales; 3) el riesgo de TC en relación con los productos químicos presentes en el agua potable y con los metales pesados. Los análisis de las características clínicas revelaron que la gran mayoría de los tumores eran neuroepiteliales (principalmente gliomas), seguidos de tumores embrionarios y meningiomas. En general, los síntomas más frecuentes fueron dolor de cabeza, seguido de signos y síntomas neurológicos focales, náuseas/ vómitos y problemas en la visión, siendo un 4% de los casos asintomáticos. El tiempo promedio de diagnóstico tendió a ser corto (mediana 1,42 meses), aunque esto varió según el tipo de tumor, la edad y el tipo de síntoma. Encontré muchos estudios que encontraron asociación entre los factores ambientales (incluido el humo del tabaco, los pesticidas y la dieta, entre otras exposiciones) y el riesgo de TC en la revisión sistemática. Sin embargo, debido a limitaciones metodológicas, la evidencia sobre el papel de estos factores en la etiología de esta enfermedad aún es incierta. Nuestros análisis en relación con los productos químicos del agua mostraron unos OR por debajo de 1 para exposiciones a THMs, y OR por encima de 1 para exposición a nitrato, tanto en períodos de exposición prenatales como postnatales, algunos estadísticamente significativos. Nuestros análisis de metales pesados mostraron ORs por debajo de 1 para la exposición al cromo. Sin embargo, la literatura es escasa sobre esta asociación. En general, esta tesis sirvió para llenar un vacío en el conocimiento sobre 1) las características clínicas de la TC en los jóvenes, útiles tanto para la práctica clínica como para la investigación etiológica; 2) causas de esta enfermedad; 3) el papel de los metales pesados y los químicos ubicuos en el agua. Se ha identificado la necesidad de realizar más investigaciones sobre la etiología y la prevención de las TC en los jóvenes.
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Richards, R. "Understanding the role of the solid tumour microenvironment in brain tumour progression." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3005579/.

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Glioblastoma (GBM) is the most common malignant brain tumour and has an extremely poor prognosis. The invasion of tumour cells into normal brain tissue makes complete surgical removal impossible; GBM is also resistant to treatment with chemotherapy and radiotherapy. Our aim was to investigate how GBM cell proliferation, survival and invasion is affected by the solid tumour microenvironment. Although GBM is highly vascularised, the abnormal structure and function of tumour blood vessels results in an inadequate supply of oxygen (hypoxia). Hypoxia is known to promote tumour progression; however, the effect of hypoxia on cell proliferation has not been well characterised. We performed a systematic investigation into the effects of different oxygen levels on the cell cycle. In contrast to the prevailing hypothesis, we found that long-term exposure to pathophysiological levels of hypoxia (1–8% O2) does not affect cell proliferation and viability and that even severe hypoxia (0.1% O2) has only minimal effects. We next sought to characterise the effect of hypoxia in multicellular tumour spheroids: 3D cell clusters that replicate important aspects of the tumour microenvironment. We characterised spheroids in terms of proliferation, survival and oxygenation and found that, in this more complex model, hypoxia was associated with reduced proliferation. We then used spheroids to develop a novel method for imaging cellular migration and invasion in 3D using lightsheet fluorescence microscopy (LSFM). We imaged spheroids over 24 h and then quantified the movements of up to 1200 cells per spheroid in terms of speed and straightness of movement. We were able to compare the movement of cells in different regions of spheroids, gaining insight into the behaviour of quiescent cells in the core of large (~500 μm), heterogeneous spheroids that had been exposed to hypoxia. This technique can be used to investigate the effect of the tumour microenvironment on cell motility and to gain insight into the mechanism of drugs that hinder the process of invasion.
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Pedapati, Praveena, and Rama Vaishnavi Tannedi. "BRAIN TUMOUR DETECTION USING HOG BY SVM." Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15905.

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Detection of a brain tumour in medical images is always a challenging task. Factors like size, shape, and position of tumour vary from different patient’s brain. So, it's important to know the exact shape, size and position of a tumour in the brain making it a challenging task for detection. Some patients exhibit high glioma (HG) type tumor while others show low glioma (LG) type. So, knowing the detailed properties of a tumour to detect them in medical images is mandatory. So far many algorithms have been implemented on how to detect and extract the tumours in medical images, they used techniques such as hybrid approach with support vector machine (SVM), back propagation and dice coefficient. Among these algorithm which used back propagation as base classifier had a highest accuracy of 90%. In this work feature extraction of the medical images of patients’ tumors in database is extracted using Histogram of Oriented Gradient, later these images are classified into tumor and non tumor images using SVM. The detection of brain tumours in patient’s image is achieved by testing the performance of SVM based on Receiver Operating Characteristics (ROC). ROC include true positive rate, true negative rate, false positive rate and false negative rate. Using ROC we calculated accuracy, sensitivity and specificity values for all the medical images of the database. For image data folder of HG in vector form, SVM gave an accuracy of 97% for 95th slice of T1 modality with high true positive rate of 0.97 remaining highest among other modalities. Whereas SVM gave an accuracy of 87% for 135th slice of T1 modality with high true positive rate of 0.8 and low false positive rate of 0.06 among other image data folder of HG. For image data folder of LG, SVM gave an accuracy of 62% for the 90th slice of FLAIR modality with the high true positive rate of 0.5 and low false positive rate of 0.25 among all others. For synthetic data folder of HG, SVM gave an accuracy of 62% for a 100th slice of FLAIR modality with the high true positive rate of 0.5 and low false positive rate of 0.06 among all others. For synthetic data folder of LG, SVM gave an accuracy of 62% for a 100th slice of FLAIR modality with the high true positive rate of 0.5 and low false positive rate of 0.06 among all others.
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Jaroudi, Rym. "Inverse Mathematical Models for Brain Tumour Growth." Licentiate thesis, Linköpings universitet, Tekniska fakulteten, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-141982.

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We study the following well-established model of reaction-diffusion type for brain tumour growth: This equation describes the change over time of the normalised tumour cell density u as a consequence of two biological phenomena: proliferation and diffusion. We discuss a mathematical method for the inverse problem of locating the brain tumour source (origin) based on the reaction-diffusion model. Our approach consists in recovering the initial spatial distribution of the tumour cells  starting from a later state , which can be given by a medical image. We use the nonlinear Landweber regularization method to solve the inverse problem as a sequence of well-posed forward problems. We give full 3-dimensional simulations of the tumour in time on two types of data, the 3d Shepp-Logan phantom and an MRI T1-weighted brain scan from the Internet Brain Segmentation Repository (IBSR). These simulations are obtained using standard finite difference discretisation of the space and time-derivatives, generating a simplistic approach that performs well. We also give a variational formulation for the model to open the possibility of alternative derivations and modifications of the model. Simulations with synthetic images show the accuracy of our approach for locating brain tumour sources.
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Jordaan, Carike. "The relationship between tumour characteristics, depressive symptoms, and neuropsychological profiles in brain tumour patients." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96700.

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Thesis (MA)--Stellenbosch University, 2015
ENGLISH ABSTRACT : Worldwide there are various reports on the prevalence of depression in patients diagnosed with brain tumours. In South Africa, psychological research in relation to psychiatric symptoms among patients with brain tumours is lacking. The aims of this study were to determine the incidence of depression in patients diagnosed with brain tumours and to clarify our understanding of the relationship between depression and tumour localisation, histopathological type of tumour, and participant characteristics. The study sample consisted of 35 patients (11 males and 24 females) aged between 21 and 64 years with a solitary primary brain tumour. The patients were treated at the neurosurgery clinics located at Tygerberg Hospital in the Western Cape and Universitas Hospital in the Free State between mid-2010 and 2013. The major histological subgroup consisted of meningiomas (47%), glioblastomas (22%), astrocytomas (19%), gliomas (9%) and epidiomas (3%). The tumour distribution was as follows: 52% in the left hemisphere, 37% in the right hemisphere, and 11 % in the midline. The psychiatric symptoms of the patients were assessed before treatment by the Beck Depression Inventory and Mini International Neuropsychiatric Interview. In addition, the patients’ neuropsychological functions were evaluated by a short neuropsychological test battery (Mini Mental State Examination, Trail Making Test (Part A), Letter Number Sequencing subtest, Hopkins Verbal Learning Test – Revised, and Brief Visuospatial Memory Test – Revised). Results from the quantitative data, showed the prevalence of mild depression was 26% for men and 43% for women. Overall 37% of the total sample had depressive symptoms. No significant relationship was found between depression and tumour location or between the various neuropsychological characteristics and neurological symptoms and tumour location. The study showed that depression is a common symptom in patients diagnosed with brain tumours and therefore depression symptoms have to be recognised and treated by psycho-educating the patients and their families, pharmacotherapy, or psychotherapy as soon as possible. However, due to the relatively small sample size, the results are of limited generalisability.
AFRIKAANSE OPSOMMING : Wêreldwyd is daar verskeie verslae oor die voorkoms van depressie in pasiënte gediagnoseer met breingewasse. In Suid-Afrika is daar ’n tekort aan sielkundige navorsing met betrekking tot psigiatriese simptome by pasiënte. Die doel van hierdie studie was om die voorkoms van depressie te bepaal in pasiënte gediagnoseer met breingewasse en om duidelikheid te kry oor die verband tussen depressie en die ligging van breingewasse, histopatologiese tipe gewas en karakter eienskappe van die deelnemers. Die steekproef van die studie het bestaan uit 35 pasiënte (11 mans en 24 vroue) tussen die ouderdomme 21 en 64 jaar met ‘n soliede breingewas. Die pasiënte is behandel by die neurochirurgiese klinieke by Tygerberg Hospitaal in die Wes-Kaap en by Universitas Hospitaal in die Vrystaat vanaf middel 2010 tot 2013. Die mees algemene histologiese subgroep het bestaan uit meningiome (47%), glioblastomas (22%), astrocytomas (19%), gliomas (9%) en epidiomas (3%). Die verspreiding van die gewasse was soos volg: 52% in die linkerhemisfeer, 37% in die regterhemisfeer en 11% in die middel. Die psigiatriese simptome van die pasiënte is voor behandeling geëvalueer met behulp van die Beck Depression Inventory en die Mini International Neuropsychiatric Interview. Bykomend is die pasiënte se neurosielkundige funksies geëvalueer met behulp van ‘n neurosielkundige toetsbattery (Mini Mental State Examination, Trail Making Test (Part A), Letter Number Sequencing subtest, Hopkins Verbal Learning Test – Revised en Brief Visuospatial Memory Test – Revised). Die resultate van die kwantitatiewe data het getoon die voorkoms van matige depressie was 26% vir mans en 43% vir vroue. In geheel het 37% van die totale steekproef depressiewe simptome getoon. Daar was geen beduidende verhouding tussen depressie en die ligging van die gewas of tussen die verskeie neurosielkundige eienskappe en die ligging van die gewas nie. Die studie het getoon dat depressie ’n algemene simptoom is in pasiënte gediagnoseer met breingewasse en daarom moet depressiewe simptome herken en so gou as moontlik behandel word deur psigo-opvoeding van die pasiënte en hul familie, farmakoterapie of psigoterapie. As gevolg van die relatiewe klein steekproef grootte het die resultate ’n beperkte veralgemeenbaarheid.
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Christidou, Maria. "The role of flavonoids on brain tumour invasion." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414780.

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Li, Xiaofei. "The IL-33/ST2 pathway in CNS : Traumatic brain injury and brain tumour." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183937.

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Interleukin 33 (IL-33) is a dual function cytokine. It is a member of the IL-1 family and it acts as a pro-inflammatory factor (18 kilo Dalton, 18 kD) like other cytokines in IL-1 family. IL-33 is also a transcription factor (32 kD - form) which can suppress or activate gene transcription in diverse cases. A variety of cell types and tissues in the central nervous system (CNS) can release IL-33 after injury. The 18 kD IL-33 binds to the membrane receptor protein ST2 ligand, then regulates downstream gene expression, triggers cytokine synthesis, and modulates the immune system response. After traumatic brain injury (TBI) in the CNS, glial cells become key players in the nervous tissue response. Astrocytes undergo activation, proliferation, release pro-inflammatory factors and, as a consequence, a glial scar barrier around the injury is formed. Simultaneously, resting microglia are activated and able to remove debris. Lastly, oligodendrocytes together with microglia and astrocytes are activated and communicate with the immune system. In addition, as a severe kind of injury to the CNS, brain tumours share some similar characteristics of brain injury, such as hypoxia and inflammation. Therefore, IL-33 may play a role in neuroinflammation and also in brain tumours. In this project, our aim was to investigate the role of IL-33 and the IL-33/ST2 pathway in traumatic brain injury and brain tumours (e.g glioma). We found that IL-33 can influence the CNS immune resonse, and may be important in CNS pathology.
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Shen, Shan. "MRI brain tumour classification using image processing and data mining." Thesis, University of Strathclyde, 2004. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21543.

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Detecting and diagnosing brain tumour types quickly and accurately is essential to any effective treatment. The general brain tumour diagnosis procedure, biopsy, not only causes a great deal of pain to the patient but also raises operational difficulty to the clinician. In this thesis, a non-invasive brain tumour diagnosis system based on MR images is proposed. The first part is image preprocessing applied to original MR images from the hospital. Non-uniformed intensity scales of MR images are standardized relying on their statistic characteristics without requiring prior or post templates. It is followed by a non-brain region removal process using morphologic operations and a contrast enhancement between white matter and grey matter by means of histogram equalization. The second part is image segmentation applied to preprocessed MR images. A new image segmentation algorithm named IFCM is developed based on the traditional FCM algorithm. Neighbourhood attractions considered in IFCM enable this new algorithm insensitive to noise, while a neural network model is designed to determine optimized degrees of attractions. This extension can also estimate inhomogenities. Brain tissue intensities are acquired from segmentation. The final part of the system is brain tumour classification. It extracts hidden diagnosis information from brain tissue intensities using a fuzzy logic based GP algorithm. This novel method imports a fuzzy membership to implement a multi-class classification directly without converting it into several binary classification problems as with most other methods. Two fitness functions are defined to describe the features of medical data precisely. The superiority of image analysis methods in each part was demonstrated on synthetic images and real MR images. Classification rules of three types and two grades of brain tumours were discovered. The final diagnosis accuracy was very promising. The feasibility and capability of the non-invasive diagnosis system were testified comprehensively.
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Mcgahan, Jennifer Anne. "Exploring memory and memory rehabilitation in paediatric brain tumour survivors." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/exploring-memory-and-memory-rehabilitation-in-paediatric-brain-tumour-survivors(194abbcb-6a1a-47aa-bbe7-8cca023f659f).html.

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This collection of studies begins by exploring the development of recognition memory in a group of healthy children and adolescents using experimental memory tests developed as part of this thesis. Various versions of these recognition memory tests were trialled in order to establish age appropriate tests for children aged 6-14 years. In keeping with previous literature in this area, these tests showed relatively stable familiarity memory throughout childhood compared to a steep developmental course for recollection memory. Paediatric brain tumour survivors are known to suffer from significant memory deficits following treatment. However, a clear description of this clinical group’s deficits, in terms of recognition and recall (and therefore also familiarity and recollection), has not previously been established. Using standard clinical memory assessments, the current body of work contributes to this area by characterising this population’s memory deficits as primarily recall-based, particularly when recalling information presented as prose. A sex difference is also noted; with female brain tumour survivors being significantly more impaired than their age-matched male counterparts. This finding is discussed with respect to the differing neural development of males and females. The experimental memory tests developed with normal children were also administered to a group of paediatric brain tumour patients. They were found to have a varied pattern of performance, including auditory recognition impairments but intact visual recognition, even when the test format incorporated similar foils. Associative memory tests revealed impairments in recollection-based recognition; this effect was dependant on the type of information being associated and the length of the encoding-test delay. A learning intervention was developed (and trialled with healthy children), using a method known as the ‘testing effect’, in an attempt to enhance recall of prose at long delays in a group of paediatric brain tumour survivors. Structured repeated retrieval was compared to repeated study for prose passages. This was found, with some patients, to be a successful method of improving recall after a delay of one week. Taken together, the work described in this thesis provides further understanding of recognition memory development in healthy children, novel insights into the residual memory function of paediatric brain tumour survivors and an exciting foundation on which to build a rehabilitation programme for this vulnerable group.
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Books on the topic "Brain tumour"

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Walker, Michael D., and David G. T. Thomas, eds. Biology of Brain Tumour. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9.

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D, Walker Michael, and Thomas D. G. T, eds. Biology of brain tumour: Proceedings of the Second International Symposium on Biology of Brain Tumour (London, October 24-26, 1984). Boston: Nijhoff, 1986.

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Group, Brain Tumour Support, ed. A sense of tumour: A collection of works by members and friends of the Brain Tumour Support Group. Dublin: E print Limited, 2000.

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On a wing and a prayer: Surviving a brain tumour. London: Janus Publishing Company, 2004.

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Banerjea, Subrata Kumar. Brain tumour cured by homoeopathy: (Dr. Banerjea's cured case study series). New Delhi: B. Jain, 1992.

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Jovanović, Dušan. Act a brain tumour and air pollution: A play in three acts. [U.K.]: [s.n.], 1995.

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Searching for heaven on the road through hell: The memoirs of a brain tumour survivor. Stony Plain, Alta: CBrownArt, 2011.

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Nagai, Masakatsu, ed. Brain Tumor. Tokyo: Springer Japan, 1996. http://dx.doi.org/10.1007/978-4-431-66887-9.

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Thomas, David G. T., and David I. Graham, eds. Malignant Brain Tumours. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-1877-0.

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Liau, Linda M., Donald P. Becker, Timothy F. Cloughesy, and Darell D. Bigner. Brain Tumor Immunotherapy. New Jersey: Humana Press, 2000. http://dx.doi.org/10.1385/1592590357.

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Book chapters on the topic "Brain tumour"

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McCool, John A. "Brain Tumour." In When Doctors Get Sick, 277–86. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4899-2001-0_30.

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Docking, Kimberley, Philippe Paquier, and Angela Morgan. "Childhood Brain Tumour." In Perspectives in Pragmatics, Philosophy & Psychology, 131–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47489-2_6.

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Paul, John. "Molecular biology of cancer." In Biology of Brain Tumour, 3–6. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9_1.

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Barnard, R. O. "Pathological classification of tumours of the central nervous system." In Biology of Brain Tumour, 77–79. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9_10.

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Lantos, P. L. "Modern neuropathological investigative techniques of brain tumours." In Biology of Brain Tumour, 81–84. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9_11.

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Racagni, Giorgio, Emilio Iuliano, Giovanni Sangiovanni, and Pietro Paoletti. "Adenylate cyclase activity and cyclic AMP content in human central nervous system tumors and in cerebrospinal fluid of patients bearing brain tumors." In Biology of Brain Tumour, 85–90. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9_12.

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Smith, B. H., M. A. Greenwood, C. J. Cummins, J. Ellis, C. Gibson, and P. L. Kornblith. "Non-nuclear cytotoxic actions of DNA cross-linking and/or alkylating agents in glioma-derived cell lines." In Biology of Brain Tumour, 91–96. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9_13.

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Adams, Carol, Dennis E. Bullard, Sandra H. Bigner, and Darell D. Bigner. "Intracerebral transplantation of D-54 MG human glioma line in immunosuppressed rats." In Biology of Brain Tumour, 97–105. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9_14.

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Rahman, Mohsen, Herman Yeger, and Laurence E. Becker. "In vivo characterization of a human neuroectodermal tumor cell line." In Biology of Brain Tumour, 107–14. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9_15.

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Weiler, Roy O., Philip V. Steart, and Isabella E. Moore. "Carbonic anhydrase C as a marker antigen in the diagnosis of choroid plexus papillomas and other tumours: an immunoperoxidase study." In Biology of Brain Tumour, 115–20. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2297-9_16.

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Conference papers on the topic "Brain tumour"

1

Thomson, Hannah, Shufan Yang, Thomas Stritch, Mitch Baldwin, Helen Mulvana, and Sandy Cochran. "Quantitative Ultrasound Differentiates Brain and Brain Tumour Phantoms." In 2019 IEEE International Ultrasonics Symposium (IUS). IEEE, 2019. http://dx.doi.org/10.1109/ultsym.2019.8925982.

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Jagannadham, Sri Lekha, K. Lakshmi Nadh, and M. Sireesha. "Brain Tumour Detection Using CNN." In 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2021. http://dx.doi.org/10.1109/i-smac52330.2021.9640875.

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Shokry, M. A., and A. M. M. A. Allam. "UWB antenna for brain stroke and brain tumour detection." In 2016 21st International Conference on Microwave, Radar and Wireless Communications (MIKON). IEEE, 2016. http://dx.doi.org/10.1109/mikon.2016.7491967.

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Sinha, Avigyan, Aneesh R P, Malavika Suresh, Nitha Mohan R, Abinaya D, and Ashwin G. Singerji. "Brain Tumour Detection Using Deep Learning." In 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII). IEEE, 2021. http://dx.doi.org/10.1109/icbsii51839.2021.9445185.

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Ugale, Vivek Dhruv, Swati S. Pawar, and Sheetal Pawar. "Brain Tumour Detection using Image Processing." In 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2022. http://dx.doi.org/10.1109/csnt54456.2022.9787643.

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Jagadeesh, Mandala, P. Chitra, K. Srilatha, M. Sumathi, and I. Rexiline Sheeba. "Brain Tumour Classification using CNN Algorithm." In 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9752096.

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Szilágyi, Tünde, Michael Brady, and Ervin Berényi. "Phase congruency map driven brain tumour segmentation." In SPIE Medical Imaging, edited by Sébastien Ourselin and Martin A. Styner. SPIE, 2015. http://dx.doi.org/10.1117/12.2082630.

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Tyagi, Manisha, and Priyanka. "Analysis of Various Brain Tumour Detection Techniques." In 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT). IEEE, 2021. http://dx.doi.org/10.1109/ccict53244.2021.00049.

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Kalyan, B., and P. Chandrasekhar Reddy. "Brain Tumour Detection Using Convolutional Neural Networks." In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2022. http://dx.doi.org/10.1109/icrito56286.2022.9965190.

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Iman Awang, Muhammad Bakhtiar, and Shafaf Ibrahim. "Comparative Analysis of Glioblastoma Brain Tumour Classification." In 2021 IEEE 9th Conference on Systems, Process and Control (ICSPC). IEEE, 2021. http://dx.doi.org/10.1109/icspc53359.2021.9689157.

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Reports on the topic "Brain tumour"

1

Anantharajan, Shenbagarajan, Shenbagalakshmi Gunasekaran, and Elamparithi Pandian. MRI Brain Tumour Segmentation Based on Fish Chaining Transition Optimization Algorithm. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2020. http://dx.doi.org/10.7546/crabs.2020.02.14.

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Hedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.

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Anantharajan, Shenbagarajan, and Shenbagalakshmi Gunasekaran. Detection and Classification of MRI Brain Tumour Using GLCM and Enhanced K-NN. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2021. http://dx.doi.org/10.7546/crabs.2021.02.13.

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Arun, Ramaiah, and Shanmugasundaram Singaravelan. Classification of Brain Tumour in Magnetic Resonance Images Using Hybrid Kernel Based Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, October 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.

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Laramore, G. E., B. R. Griffin, and A. Spence. American brain tumor patients treated with BNCT in Japan. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/421335.

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Lojzim, Joshua Michael, and Marcus Fries. Brain Tumor Segmentation Using Morphological Processing and the Discrete Wavelet Transform. Journal of Young Investigators, August 2017. http://dx.doi.org/10.22186/jyi.33.3.55-62.

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Majewska, Anna, and Edward B. Brown. The Influence of Neuronal Activity on Breast Tumor Metastasis to the Brain. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada502596.

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Majewska, Anna K., and Edward B. Brown. The Influence of Neuronal Activity on Breast Tumor Metastasis to the Brain. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada513293.

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Zamenhof, R. G. (A clinical trial of neutron capture therapy for brain tumors). Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/7262961.

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Zamenhof, R. G. (A clinical trial of neutron capture therapy for brain tumors). Office of Scientific and Technical Information (OSTI), January 1988. http://dx.doi.org/10.2172/7276865.

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