Relevant bibliographies by topics / CLASSIFICATION OF BRAIN TUMOR

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  2. Dissertations / Theses
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Academic literature on the topic 'CLASSIFICATION OF BRAIN TUMOR'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "CLASSIFICATION OF BRAIN TUMOR"

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Manasa, P. Venkata Sai, J. Jeevitha, M. Lakshmi Chandana, M. Jeevana Sravanthi, and M. Ali Shaik. "Brain Tumor Radiogenomic Classification Using Deep Learning." International Journal of Research Publication and Reviews 4, no. 3 (March 17, 2023): 1830–36. http://dx.doi.org/10.55248/gengpi.2023.4.33058.

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A, Ms Vidhya, Dr Parameswari R, and Ms Sathya S. "Brain Tumor Classification Using Various Machine Learning Algorithms." International Journal of Research in Arts and Science 5, Special Issue (August 30, 2019): 258–70. http://dx.doi.org/10.9756/bp2019.1002/25.

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Punam, Saudagar. "Deep Learning Approach for Brain Tumor Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3094–98. http://dx.doi.org/10.22214/ijraset.2021.35648.

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Tumors are complex. There are a lot of variations in sizes and location of tumor. This makes it really hard for complete understanding of tumor. Brain tumour is the abnormal growth of cells inside the brain cranium which limits the functioning of brain. Now a days, medical images processing is a most challenging and developing field. Automated detection of tumor in MRI is extremely crucial because it provides information about abnormal tissues which is important for planning treatment. The conventional method for defect detection in resonance brain images is time consuming. So, automated tumor detection methods are developed because it would save radiologist time and acquire a tested accuracy. The MRI brain tumor detection is complicated task due to complexity and variance of tumors.There are many previously implemented approaches on detecting these kinds of brain tumors. In this paper, we used and implement Convolutional Neural Network (CNN) which is one among the foremost widely used deep learning architectures for classifying a brain tumor into four types. i.e Glioma , Meningioma, Pituitary and No tumour. CNN may be used to effectively locate most cancers cells in brain via MRI. classification.
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Pol, Jay. "Brain Tumor Image Classification using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1934–41. http://dx.doi.org/10.22214/ijraset.2022.44191.

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Abstract: We present a method for segmenting and categorizing brain tumors in the challenge of content of brain tumor with segmentation is enrolled and skull is exposed for bar graph equivalent high-level contradiction refer amount. Preprocessing, segmentation, feature extraction, optimization, and classification are used to detect tumors. The tissue is then classified using preprocessed images. We utilized leave-one-out cross-validation to generate a Dice overlap of 88 for the whole tumor area, 75 for the core tumor region, and 95 for the enhancing tumor region, which is higher than the Dice overlap reported
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Dozic, Slobodan, Dubravka Cvetkovic-Dozic, Milica Skender-Gazibara, and Branko Dozic. "Review of the World Health Organization classification of tumors of the nervous system." Archive of Oncology 10, no. 3 (2002): 175–77. http://dx.doi.org/10.2298/aoo0203175d.

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(Conclusion) Classifications of the nervous system tumors should be neither static nor definitive. The most recent, third, current WHO classification of nervous system tumors was published in 2000. Many substantial changes were introduced. New entities include the chordoid glioma of the third ventricle, the atypical teratoid/rhabdoid tumor, cerebellar liponeurocytoma (the former lipomatous medulloblstoma of the cerebellum), solitary fibrous tumor and perineurioma. The new tumor variants include the large cell medulloblastoma, tanacytic ependymoma and rhabdoid meningioma. Several essential changes were introduced in the meningiomas regarding histological subtypes, grading and proliferation index. In addition to new entities described in the 2000 WHO classification there are newly brain tumor entities and tumor variants, which are not included in the current classification due to the insufficient number of reporeted cases, for example papillary glioneuronal tumor, rosetted glioneuronal tumor, lipoastrocytoma and lipomatous meningioma. They will be probably accepted in the next WHO classificaton. In the current WHO classification the importance of cytogenetic and molecular biologic investigation in the understanding and further classifications of these tumors has been emphasized.
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Narawade, Vaibhav, Chaitali Shetty, Purva Kharsambale, Samruddhi Bhosale, and Sushree Rout. "Brain Tumor Classification using Transfer Learning." Journal of Trends in Computer Science and Smart Technology 5, no. 3 (September 2023): 223–47. http://dx.doi.org/10.36548/jtcsst.2023.3.002.

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Brain tumors are one of the more severe medical conditions that can affect both children and adults. Brain tumors make up between 85 and 90 percent of all primary Central Nervous System (CNS) malignancies. Each year, brain tumors are found in about 11,700 persons. The 5-year survival rate is around 34% for males and 36% for female patients with malignant brain or CNS tumors. Brain tumors can be classified as benign, malignant, pituitary, and other forms. Appropriate treatment, meticulous planning, and exact diagnostics must be used to prolong patient lives. The most reliable way for detecting brain cancer is Magnetic Resonance Imaging (MRI). The images are examined by the radiologist. As brain tumors are complex the MRI serve as guide to diagnose the seriousness of the disease. Since the placement and size of the brain tumor seems incredibly abnormal for persons affected by the disease it becomes difficult to properly comprehend the nature of the tumor. For MRI analysis, a qualified neurosurgeon is also necessary. Compiling the results of an MRI can be extremely difficult and time-consuming because there are typically not enough qualified medical professionals and individuals who are knowledgeable about malignancy in poor countries. Thus, this issue can be resolved by an automated cloud-based solution. In the proposed model, The Convolutional Neural Networks (CNN) is used for the classification of the brain tumor dataset with an accuracy of 99%.
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Wedad Abdul Khuder Naser *. "Brain tumor classification and diagnosis techniques." Global Journal of Engineering and Technology Advances 10, no. 2 (February 28, 2022): 071–74. http://dx.doi.org/10.30574/gjeta.2022.10.2.0036.

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One of the leading causes of increased mortality in both children and adults is a brain tumor. Tumor is a severe issue that has taken over the usual force that controls growth. On MRI pictures, there are several techniques for classification and detecting a brain tumor region. We present background reviews of many proposed techniques for detecting brain tumors in this paper. There is a lot of literature on diagnosing and improving the accuracy of this type of brain tumor.
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Kadam, Ankita. "Brain Tumor Classification using Deep Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 417–26. http://dx.doi.org/10.22214/ijraset.2021.39280.

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Abstract: A Brain tumor is one aggressive disease. An estimated more than 84,000 people will receive a primary brain tumor diagnosis in 2021 and an estimated 18,600 people will die from a malignant brain tumor (brain cancer) in 2021.[8] The best technique to detect brain tumors is by using Magnetic Resonance Imaging (MRI). More than any other cancer, brain tumors can have lasting and life-altering physical, cognitive, and psychological impacts on a patient’s life and hence faster diagnosis and best treatment plan should be devised to improve the life expectancy and well-being of these patients. Neural networks have shown colossal accuracy in image classification and segmentation problems. In this paper, we propose comparative studies of various deep learning models based on different types of Neural Networks(ANN, CNN, TL) to firstly identify brain tumors and then classify them into Benign Tumor, Malignant Tumor or Pituitary Tumor. The data set used holds 3190 images on T1-weighted contrast-enhanced images which were cleaned and augmented. The best ANN model concluded with an accuracy of 78% and the best CNN model consisting of 3 convolution layers had an accuracy of 90%. The VGG16(retrained on the dataset) model surpasses other ANN, CNN, TL models for multi-class tumor classification. This proposed network achieves significantly better performance with a validation accuracy of 94% and an F1-Score of 91. Keywords: Artificial Neural Network(ANN), Convolution Neural Network (CNN), Transfer Learning(TL), Magnetic Resonance Imaging(MRI.)
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A., Afreen Habiba. "Diagnosis of Brain Tumor using Semantic Segmentation and Advance-CNN Classification." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1204–24. http://dx.doi.org/10.37200/ijpr/v24i5/pr201795.

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Havaei, Mohammad, Hugo Larochelle, Philippe Poulin, and Pierre-Marc Jodoin. "Within-brain classification for brain tumor segmentation." International Journal of Computer Assisted Radiology and Surgery 11, no. 5 (November 3, 2015): 777–88. http://dx.doi.org/10.1007/s11548-015-1311-1.

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Dissertations / Theses on the topic "CLASSIFICATION OF BRAIN TUMOR"

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Kalvakolanu, Anjaneya Teja Sarma. "Brain Tumor Detection and Classification from MRI Images." DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2267.

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A brain tumor is detected and classified by biopsy that is conducted after the brain surgery. Advancement in technology and machine learning techniques could help radiologists in the diagnosis of tumors without any invasive measures. We utilized a deep learning-based approach to detect and classify the tumor into Meningioma, Glioma, Pituitary tumors. We used registration and segmentation-based skull stripping mechanism to remove the skull from the MRI images and the grab cut method to verify whether the skull stripped MRI masks retained the features of the tumor for accurate classification. In this research, we proposed a transfer learning based approach in conjunction with discriminative learning rates to perform the classification of brain tumors. The data set used is a 3064 T MRI images dataset that contains T1 flair MRI images. We achieved a classification accuracy of 98.83%, 96.26%, and 95.18% for training, validation, and test sets and an F1 score of 0.96 on the T1 Flair MRI dataset.
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Chang, Spencer J. "Brain Tumor Classification Using Hit-or-Miss Capsule Layers." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2006.

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The job of classifying or annotating brain tumors from MRI images can be time-consuming and difficult, even for radiologists. To increase the survival chances of a patient, medical practitioners desire a means for quick and accurate diagnosis. While datasets like CIFAR, ImageNet, and SVHN have tens of thousands, hundreds of thousands, or millions of samples, an MRI dataset may not have the same luxury of receiving accurate labels for each image containing a tumor. This work covers three models that classify brain tumors using a combination of convolutional neural networks and of the concept of capsule layers. Each network utilizes a hit-or-miss capsule layer to relate classes to capsule vectors in a one-to-one relationship. Additionally, this work proposes the use of deep active learning for picking the samples that can give the best model, PSP-HitNet, the most information when adding mini-batches of unlabeled data into the master, labeled training dataset. By using an uncertainty estimated querying strategy, PSP-HitNet approaches the best validation accuracy possible within the first 12-24% of added data from the unlabeled dataset, whereas random choosing takes until 30-50% of the unlabeled to reach the same performance.
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Kampouraki, Vasileia. "Patch-level classification of brain tumor tissue in digital histopathology slides with Deep Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177361.

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Histopathology refers to the visual inspection of tissue under the microscope and it is the core part of diagnosis. The process of manual inspection of histopathology slides is very time-consuming for pathologists and error-prone. Furthermore, diagnosis can sometimes differ among specialists. In recent years, convolutional neural networks (CNNs) have demonstrated remarkable performances in the classification of digital histopathology images. However, due to their high resolution, whole-slide images are of immense size, often gigapixels, making it infeasible to train CNNs directly on them. For that, patch-level classification is used instead. In this study, a deep learning approach for patch-level classification of glioblastoma (i.e. brain cancer) tissue is proposed. Four different state-of-the-art models were evaluated (MobileNetV2, ResNet50, ResNet152V2, and VGG16), with MobileNetV2 being the best among them, achieving 80% test accuracy. The study also proposes a scratch-trained CNN architecture, inspired by the popular VGG16 model, which achieved 81% accuracy. Both models scored 87% test accuracy when trained with data augmentation. All models were trained and tested on randomly sampled patches from the Ivy GAP dataset, which consisted of 724 H&E images in total. Finally, as patch-level predictions cannot be used explicitly by pathologists, prediction results from two slides were presented in the form of whole-slide images. Post-processing was also performed on those two predicted WSIs in order to make use of the spatial correlations among the patches and increase the classification accuracy. The models were statistically compared using the Wilcoxon signed-rank test.
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Hrabovszki, Dávid. "Classification of brain tumors in weakly annotated histopathology images with deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177271.

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Brain and nervous system tumors were responsible for around 250,000 deaths in 2020 worldwide. Correctly identifying different tumors is very important, because treatment options largely depend on the diagnosis. This is an expert task, but recently machine learning, and especially deep learning models have shown huge potential in tumor classification problems, and can provide fast and reliable support for pathologists in the decision making process. This thesis investigates classification of two brain tumors, glioblastoma multiforme and lower grade glioma in high-resolution H&E-stained histology images using deep learning. The dataset is publicly available from TCGA, and 220 whole slide images were used in this study. Ground truth labels were only available on whole slide level, but due to their large size, they could not be processed by convolutional neural networks. Therefore, patches were extracted from the whole slide images in two sizes and fed into separate networks for training. Preprocessing steps ensured that irrelevant information about the background was excluded, and that the images were stain normalized. The patch-level predictions were then combined to slide level, and the classification performance was measured on a test set. Experiments were conducted about the usefulness of pre-trained CNN models and data augmentation techniques, and the best method was selected after statistical comparisons. Following the patch-level training, five slide aggregation approaches were studied, and compared to build a whole slide classifier model. Best performance was achieved when using small patches (336 x 336 pixels), pre-trained CNN model without frozen layers, and mirroring data augmentation. The majority voting slide aggregation method resulted in the best whole slide classifier with 91.7% test accuracy and 100% sensitivity. In many comparisons, however, statistical significance could not be shown because of the relatively small size of the test set.
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Kanli, Georgia. "Method for the classification of brain cancer treatment's responsiveness via physical parameters of DCE-MRI data." Thesis, Stockholms universitet, Fysikum, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-116822.

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Tumors have several important hallmarks; anomalous and heterogeneous behaviors of their vascular structures, and high angiogenesis and neovascularization. Tumor tissue presents high blood flow (F) and extraction ratio (E) of contrast molecules. Consequently there is growing interest in non invasive methods for characterizing changes in tumor vasculature. Toft's model has been extensively used in the past in order to calculate Ktrans maps which take into consideration both F and E. However, in this thesis we argue that for accurate tumor characterization we need a model able to compute both F and E in tissue plasma. This project has been developed as part of a larger project, working toward building a Clinical Decision Support System (CDSS): an interactive expert computer software, that helps doctors and other health professionals make decisions regarding patient treatment progress. Using the Gamma Capillary Transit Time (GCTT) pharmacokinetic model we calculate F and E separately in a more realistic framework; unlike other models it takes into account the heterogeneity of the tumor, which depends on parameter a-1. a-1 is the width of the distribution of the capillary transit times within a tissue voxel. In more detail, a-1 expresses the heterogeneity of tissue microcirculation and microvasculature. We studied 9 patients pathologically diagnosed with glioblastoma multiforme (GBM), a common malignant type of brain tumor. Several physiological parameters including the blood flow and extraction ratio distributions were calculated for each patient. Then we investigated if these parameters can characterize early the patients' responsiveness to current treatment; we assessed the classification potential based on the actual therapy outcome. To this end, we present a novel analysis framework which exploits the new parameter a-1 and organizes each voxel into four sub-region. Our results indicate that early characterization of response based on GCCT can be significantly improved by focusing on tumor voxels from a specific sub-region.
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Kirsch, Matthias, Thomas Santarius, Peter M. Black, and Gabriele Schackert. "Therapeutic Anti-Angiogenesis for Malignant Brain Tumors." Karger, 2001. https://tud.qucosa.de/id/qucosa%3A27619.

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Malignant brain tumors, especially malignant gliomas, have a poor prognosis, a fact which has remained unchanged over the last decades despite the employment of multimodal therapeutic approaches. Malignant gliomas are among the most vascularized tumors known and the amount of vascularization has been correlated to their prognosis. Since tumor growth is dependent on concomitant vascularization, recent experimental studies have focused on the use of anti-angiogenic molecules as a novel strategy in brain tumor therapy. Angiogenesis inhibitors target at proliferating endothelial cells and suppress the formation of a sufficient vascular bed. Inhibitors such as TNP-470, suramin and angiostatin have shown their therapeutic potential in experimental studies. In a clinical setting, they could be applied for the treatment of multiple tumors or postsurgically as an adjuvant therapy to prevent recurrence. This article discusses presently available anti-angiogenic agents, emphasizing on substances already in clinical trials.
Maligne Hirntumoren, insbesondere die malignen Gliome, haben trotz multimodaler Therapieansätze eine unverändert schlechte Prognose. Diese Aggressivität korreliert mit der Tatsache, daß maligne Gliome zu den gefäßreichsten Tumoren zählen, die wir kennen. Die Quantifizierung der Gefäßdichte in diesen Tumoren erlaubte die Korrelation zur Überlebenszeit der Patienten. Da das Tumorwachstum von einer begleitenden Neovaskularisierung abhängt, wurden erste experimentelle Therapieansätze durchgeführt, um das Tumorwachstum durch Inhibierung der Neovaskularisierung zu verhindern. Inhibitoren der Angiogenese, z.B. TNP-470, Suramin und Angiostatin hemmen die Proliferation von Endothelzellen und die Ausbildung eines funktionsfähigen Gefäßbettes. Erste experimentelle Ansätze haben ihre tumorstatische Wirksamkeit in vivo bewiesen. Zur klinischen Behandlung wären diese Substanzen in Verbindung mit bestehenden Therapien einsetzbar, insbesondere für die Behandlung multipler Tumoren und zur postoperativen Therapie. Diese Übersichtsarbeit beschreibt die neuesten anti-angiogenen Therapiekonzepte besonders mit Hinblick auf Substanzen, die in ersten klinischen Studien eingesetzt werden.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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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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Zhang, Nan. "Feature selection based segmentation of multi-source images : application to brain tumor segmentation in multi-sequence MRI." Phd thesis, INSA de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00701545.

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Multi-spectral images have the advantage of providing complementary information to resolve some ambiguities. But, the challenge is how to make use of the multi-spectral images effectively. In this thesis, our study focuses on the fusion of multi-spectral images by extracting the most useful features to obtain the best segmentation with the least cost in time. The Support Vector Machine (SVM) classification integrated with a selection of the features in a kernel space is proposed. The selection criterion is defined by the kernel class separability. Based on this SVM classification, a framework to follow up brain tumor evolution is proposed, which consists of the following steps: to learn the brain tumors and select the features from the first magnetic resonance imaging (MRI) examination of the patients; to automatically segment the tumor in new data using a multi-kernel SVM based classification; to refine the tumor contour by a region growing technique; and to possibly carry out an adaptive training. The proposed system was tested on 13 patients with 24 examinations, including 72 MRI sequences and 1728 images. Compared with the manual traces of the doctors as the ground truth, the average classification accuracy reaches 98.9%. The system utilizes several novel feature selection methods to test the integration of feature selection and SVM classifiers. Also compared with the traditional SVM, Fuzzy C-means, the neural network and an improved level set method, the segmentation results and quantitative data analysis demonstrate the effectiveness of our proposed system.
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Vicente, Robledo Javier. "Clinical Decision Support Systems for Brain Tumour Diagnosis: Classification and Evaluation Approaches." Doctoral thesis, Editorial Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/17468.

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A lo largo de las últimas décadas, la disponibilidad cada vez mayor de grandes cantidades de información biomédica ha potenciado el desarrollo de herramientas que permiten extraer e inferir conocimiento. El aumento de tecnologías biomédicas que asisten a los expertos médicos en sus decisiones ha contribuido a la incorporación de un paradigma de medicina basada en la evidencia centrada en el paciente. Las contribuciones de esta Tesis se centran en el desarrollo de herramientas que asisten al médico en su proceso de toma de decisiones en el diagnóstico de tumores cerebrales (TC) mediante Espectros de Resonancia Magnética (ERM). En esta Tesis se contribuye con el desarrollo de clasificadores basados en Reconocimiento de Patrones (RP) entrenados con ERM de pacientes pediátricos y adultos para establecer el tipo y grado del tumor. Estos clasificadores especializados son capaces de aprovechar las diferencias bioquímicas existentes entre los TC infantiles y de adultos. Una de las principales contribuciones de esta Tesis consiste en el desarrollo de modelos de clasificación enfocados a discriminar los tres tipos de tumores cerebrales pediátricos más prevalentes. El cerebelo suele ser una localización habitual de estos tumores, resultando muy difícil distinguir el tipo mediante el uso de Imagen de Resonancia Magnética. Por lo tanto, obtener un alto acierto en la discriminación de astrocitomas pilocíticos, ependimomas y meduloblastomas mediante ERM resulta crucial para establecer una estrategia de cirugía, ya que cada tipo de tumor requiere de unas acciones diferentes si se quiere obtener un buen pronóstico del paciente. Asimismo, esta Tesis contribuye en la extracción de características para ERM mediante el estudio de la combinación de señales de ERM adquiridas en dos tiempos de eco: tiempo de eco corto y tiempo de eco largo; concluyendo que dicha combinación mejora la clasificación de tumores cerebrales pediátricos frente al hecho de usar únicamente los ERM de un
Vicente Robledo, J. (2012). Clinical Decision Support Systems for Brain Tumour Diagnosis: Classification and Evaluation Approaches [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17468
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Alberts, Esther [Verfasser], Björn [Akademischer Betreuer] Menze, Björn [Gutachter] Menze, and Claus [Gutachter] Zimmer. "Multi-modal Multi-temporal Brain Tumor Segmentation, Growth Analysis and Texture-based Classification / Esther Alberts ; Gutachter: Björn Menze, Claus Zimmer ; Betreuer: Björn Menze." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/118744393X/34.

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Books on the topic "CLASSIFICATION OF BRAIN TUMOR"

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K, Kokula Krishna Hari, ed. An Image Segmentation and Classification for Brain Tumor Detection using Pillar K-Means Algorithm. Chennai, India: Association of Scientists, Developers and Faculties, 2016.

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1913-, Fields William S., ed. Primary brain tumors: A review of histologic classification. New York: Springer-Verlag, 1989.

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Buell, Duncan A. Binary quadratic forms: Classical theory and modern computations. New York: Springer-Verlag, 1989.

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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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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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Goldfarb, Ronald H., ed. Brain Tumor Invasiveness. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2622-3.

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Liau, Linda M., Donald P. Becker, Timothy F. Cloughesy, and Darell D. Bigner, eds. Brain Tumor Immunotherapy. Totowa, NJ: Humana Press, 2001. http://dx.doi.org/10.1007/978-1-59259-035-3.

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Hattingen, Elke, and Ulrich Pilatus, eds. Brain Tumor Imaging. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-642-45040-2.

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H, Goldfarb Ronald, ed. Brain tumor invasiveness. Dordrecht: Kluwer Academic, 1994.

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Singh, Sheila K., and Chitra Venugopal, eds. Brain Tumor Stem Cells. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8805-1.

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Book chapters on the topic "CLASSIFICATION OF BRAIN TUMOR"

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Wechsler, Wolfgang, and Guido Reifenberger. "Histopathological Classification of Brain Tumors According to the Revised WHO Classification: Current State and Perspectives." In Brain Tumor, 3–20. Tokyo: Springer Japan, 1996. http://dx.doi.org/10.1007/978-4-431-66887-9_1.

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Lerousseau, Marvin, Eric Deutsch, and Nikos Paragios. "Multimodal Brain Tumor Classification." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 475–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72087-2_42.

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Kathawala, Fatema, Ami Shah, Jugal Shah, Shranik Vora, and Sonali Patil. "Brain Tumor Detection and Classification." In Advances in Computing and Intelligent Systems, 547–56. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0222-4_52.

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Wechsler, W., and G. Reifenberger. "Immunohistochemistry in Brain Tumor Classification." In Neuro-Oncology, 11–19. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3152-0_2.

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Teoh, Teik Toe. "CNN for Brain Tumor Classification." In Convolutional Neural Networks for Medical Applications, 19–34. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8814-1_2.

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Patil, Saraswati, Sangita Jaybhaye, Sanjyot Kotgire, Shravan Raina, Somanshu Bhat, and Saksham Sharma. "Brain Tumor Detection and Classification." In IOT with Smart Systems, 379–91. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3761-5_35.

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Pfister, Stefan M., David Capper, and David T. W. Jones. "Modern Principles of CNS Tumor Classification." In Brain Tumors in Children, 117–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-43205-2_6.

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Waghmare, Vishal K., and Maheshkumar H. Kolekar. "Brain Tumor Classification Using Deep Learning." In Studies in Big Data, 155–75. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4112-4_8.

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Meena, S. Divya, Srirama V. S. S. Bulusu, V. Sai Siddharth, S. Prathik Reddy, and J. Sheela. "Brain Tumor Classification Using Transfer Learning." In Machine Learning and Artificial Intelligence in Healthcare Systems, 191–209. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003265436-9.

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Bahuguna, Aman, Azhar Ashraf, Kavita, Sahil Verma, and Poonam Negi. "Brain Tumor Classification from MRI Scans." In International Conference on Innovative Computing and Communications, 713–25. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3010-4_57.

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Conference papers on the topic "CLASSIFICATION OF BRAIN TUMOR"

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Amin, Javeria, Muhammad Sharif, Mudassar Raza, Tanzila Saba, and Amjad Rehman. "Brain Tumor Classification: Feature Fusion." In 2019 International Conference on Computer and Information Sciences (ICCIS). IEEE, 2019. http://dx.doi.org/10.1109/iccisci.2019.8716449.

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Jairam, S. J. A., D. Lokeshwar, B. Divya, and P. Mohamed Fathimal. "Brain Tumor Detection Using Deep Learning." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-5d1g8v.

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Brain tumors are developed as a result of unregulated and fast cell proliferation. It may result in death if not treated in the early stages. The imaging technology used to diagnose brain tumors is known as magnetic resonance imaging (MRI). Early detection of brain tumors is critical in medical practise in order to determine whether the tumor will progress to malignancy. For picture categorization, deep learning is a useful and effective method. Deep learning has been widely used in a variety of sectors, including medical imaging, because its application does not necessitate the expertise of a subject matter expert, but does necessitate a large amount of data and a variety of data in order to produce accurate classification results. The deep learning technique for image categorization is the convolutional neural network (CNN).In this research work , two different models are used to categorize brain tumors and their results were evaluated using performance metrics like accuracy and precision and the results were impressive
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Paul, Justin S., Andrew J. Plassard, Bennett A. Landman, and Daniel Fabbri. "Deep learning for brain tumor classification." In SPIE Medical Imaging, edited by Andrzej Krol and Barjor Gimi. SPIE, 2017. http://dx.doi.org/10.1117/12.2254195.

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Saleh, Ahmad, Rozana Sukaik, and Samy S. Abu-Naser. "Brain Tumor Classification Using Deep Learning." In 2020 International Conference on Assistive and Rehabilitation Technologies (iCareTech). IEEE, 2020. http://dx.doi.org/10.1109/icaretech49914.2020.00032.

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Chaitanya, Koganti, Kolisetty Sai Saran, Inapanurthi Swarupa, and G. Jaya Lakshmi. "Brain Tumor Classification using DeepResidual Learning." In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2022. http://dx.doi.org/10.1109/iciccs53718.2022.9787993.

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Rochmawati, Naim, Hanik Badriyah Hidayati, Yuni Yamasari, Wiyli Yustanti, I. Made Suartana, Agus Prihanto, and Aditya Prapanca. "Brain Tumor Classification Using Transfer Learning." In 2022 Fifth International Conference on Vocational Education and Electrical Engineering (ICVEE). IEEE, 2022. http://dx.doi.org/10.1109/icvee57061.2022.9930403.

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Simon, Eliott, and Alexia Briassouli. "Vision Transformers for Brain Tumor Classification." In 9th International Conference on Bioimaging. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010834300003123.

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Venkata Subbarao, M., G. Challa Ram, D. Girish Kumar, and Sudheer Kumar Terlapu. "Brain Tumor Classification using Ensemble Classifiers." In 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9752177.

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Sorte, Ashish, Ruchita Sathe, Shubham Yadav, and Chitra Bhole. "Brain Tumor Classification using Deep Learning." In 2022 5th International Conference on Advances in Science and Technology (ICAST). IEEE, 2022. http://dx.doi.org/10.1109/icast55766.2022.10039550.

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Shaji, Thejus, K. Ravi, E. Vignesh, and A. Sinduja. "Brain Tumor Segmentation Using Modified Double U-Net Architecture." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-52096g.

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Children and the elderly are most susceptible to brain tumors. It's deadly cancer caused by uncontrollable brain cell proliferation inside the skull. The heterogeneity of tumor cells makes classification extremely difficult. Image segmentation has been revolutionized because of the Convolution Neural Network (CNN), which is especially useful for medical images. Not only does the U-Net succeed in segmenting a wide range of medical pictures in general, but also in some particularly difficult instances. However, we uncovered severe problems in the standard models that have been used for medical image segmentation. As a result, we applied modification and created an efficient U-net-based deep learning architecture, which was examined on the Brain Tumor dataset from the Kaggle repository, which consists of over 1500 images of brain tumors together with their ground truth. After comparing our model to comparable cutting-edge approaches, we determined that our design resulted in at least a 10% improvement, showing that it generates more efficient, better, and robust results.
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Reports on the topic "CLASSIFICATION OF BRAIN TUMOR"

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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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Li, Xiao-Nan. Harnessing Autopsied DIPG Tumor Tissues for Orthotopic Xenograft Model Development in the Brain Stems of SCID Mice. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada568355.

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Phillips, Peter C. Early Detection of NF1 Brain Tumor Growth and Treatment Response by MRI, MRS and PET in a Trial of Novel Antitumor Drugs. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada376214.

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Tian, Cong, Jianlong Shu, Wenhui Shao, Zhengxin Zhou, Huayang Guo, and Jingang Wang. The efficacy and safety of IL Inhibitors, TNF-α Inhibitors, and JAK Inhibitor on ankylosing spondylitis: A Bayesian network meta-analysis of a “randomized, double-blind, placebo-controlled” trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0117.

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Review question / Objective: In this study, we conducted a Bayesian network meta-analysis to evaluate the efficacy and safety of interleukin (IL) inhibitors, tumor necrosis factor-alpha (TNF-α) inhibitors, and Janus kinase (JAK) inhibitors on ankylosing spondylitis (AS).The purpose of this study is to compare the effectiveness and safety of different interventions for treating AS to provide insights into the decision-making in clinicalpractice. Condition being studied: Ankylosing spondylitis. Based on the Bayesian hierarchical model, we conducted a network meta-analysis using the gemtc package in R software (version 4.1.3) and Stata software (version 15.1). Cong Tian and Jianlong Shu contributed to the conception and design of the study and supervised the tweet classification. All authors drafted the manuscript. Wenhui Shao, Zhengxin Zhou, Huayang Guo and Jingang Wang contributed to data management and tweet classification. Cong Tian, Jianlong Shu and Zhengxin Zhou performed the statistical analysis. Cong Tian, Jianlong Shu, Wenhui Shao and Zhengxin Zhou reviewed the manuscript.
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