Literatura académica sobre el tema "LUNG PATTERN CLASSIFICATION"
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Artículos de revistas sobre el tema "LUNG PATTERN CLASSIFICATION"
Mangayarkarasi, T., R. Chithrakkannan y R. Karthikeyan. "PATTERN KNOWLEDGE DISCOVERY BASED LUNG CANCER CLASSIFICATION SYSTEM". CARDIOMETRY, n.º 26 (1 de marzo de 2023): 623–28. http://dx.doi.org/10.18137/cardiometry.2023.26.623628.
Texto completoAjin, M. y L. Mredhula. "Diagnosis Of Interstitial Lung Disease By Pattern Classification". Procedia Computer Science 115 (2017): 195–208. http://dx.doi.org/10.1016/j.procs.2017.09.126.
Texto completoBoukansa, Sara, Zineb Benbrahim, Sanaa Gamrani, Sanae Bardai, Laila Bouguenouch, Asmae Mazti, Nadia Boutahiri et al. "Correlation of Epidermal Growth Factor Receptor Mutation With Major Histologic Subtype of Lung Adenocarcinoma According to IASLC/ATS/ERS Classification". Cancer Control 29 (enero de 2022): 107327482210849. http://dx.doi.org/10.1177/10732748221084930.
Texto completoSHAMSHEYEVA, ALENA, ARCOT SOWMYA y PETER WILSON. "SEGMENTATION OF LUNG PATTERNS IN HIGH-RESOLUTION COMPUTED TOMOGRAPHY IMAGES OF THE LUNG". International Journal of Computational Intelligence and Applications 07, n.º 03 (septiembre de 2008): 265–80. http://dx.doi.org/10.1142/s1469026808002259.
Texto completoSubramaniam, Umashankar, M. Monica Subashini, Dhafer Almakhles, Alagar Karthick y S. Manoharan. "An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques". BioMed Research International 2021 (13 de noviembre de 2021): 1–17. http://dx.doi.org/10.1155/2021/1896762.
Texto completoAnthimopoulos, Marios, Stergios Christodoulidis, Lukas Ebner, Andreas Christe y Stavroula Mougiakakou. "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network". IEEE Transactions on Medical Imaging 35, n.º 5 (mayo de 2016): 1207–16. http://dx.doi.org/10.1109/tmi.2016.2535865.
Texto completoUmmay Atiya, S. y N. V.K Ramesh. "Pattern classification of interstitial lung disease in high resolution clinical datasets: A systematic review". International Journal of Engineering & Technology 7, n.º 2.7 (18 de marzo de 2018): 114. http://dx.doi.org/10.14419/ijet.v7i2.7.10275.
Texto completoRaparia, Kirtee y Rishi Raj. "Tissue Continues to Be the Issue: Role of Histopathology in the Context of Recent Updates in the Radiologic Classification of Interstitial Lung Diseases". Archives of Pathology & Laboratory Medicine 143, n.º 1 (1 de enero de 2019): 30–33. http://dx.doi.org/10.5858/arpa.2018-0134-ra.
Texto completoKhoiro, M., R. A. Firdaus, E. Suaebah, M. Yantidewi y Dzulkiflih. "Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network". Journal of Physics: Conference Series 2392, n.º 1 (1 de diciembre de 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2392/1/012024.
Texto completoVasconcelos, Verónica, João Barroso, Luis Marques y José Silvestre Silva. "Enhanced Classification of Interstitial Lung Disease Patterns in HRCT Images Using Differential Lacunarity". BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/672520.
Texto completoTesis sobre el tema "LUNG PATTERN CLASSIFICATION"
Froz, Bruno Rodrigues. "CLASSIFICAÇÃO DE NÓDULOS PULMONARES UTILIZANDO VIDAS ARTIFICIAIS, MVS E MEDIDAS DIRECIONAIS DE TEXTURA". Universidade Federal do Maranhão, 2015. http://tedebc.ufma.br:8080/jspui/handle/tede/285.
Texto completoConselho Nacional de Desenvolvimento Científico e Tecnológico
The lung cancer is known for presenting the highest mortality rate and one of the lowest survival rate after diagnosis, which is mainly caused by the late detection and treatment. With the goal of assist the lung cancer specialists, computed aided diagnosis systems are developed to automate the detection and diagnosis of this disease. This work proposes a methodology to classify, with computed tomography images, lung nodules candidates and non-nodules candidates. The Lung Image Database Consortium (LIDC) image database is used to create an image database with nodules candidates and an image database with non-nodule candidates. Three techniques are utilized to extract texture measurements. The first one is the artificial life algorithm Artificial Crawlers. The second one is the use of Rose Diagram to extract directional measurements. The third and last one is an hybrid model to join the Artificial Crawlers and Rose Diagram texture measurements. In the classification, que Support Vector Machine classifier is used, with its radial basis kernel. The archived results are very promising. With 833 LIDC exams, divided in 60% for train and 40% for test, we reached na accuracy mean of 94,30%, sensitivity mean of 91,86%, specificity mean of 94,78%, variance coefficient of accuracy of 1,61% and ROC curves mean área of 0,922.
O câncer de pulmão é conhecido por apresentar a maior taxa de mortalidade e uma das menores taxas de sobrevida após o diagnóstico, o que é causado principalmente pela detecção e tratamento tardios. Para o auxílio dos especialistas em câncer pulmonar, são desenvolvidos sistemas de diagnósticos auxiliados por computador com o objetivo de automatizar a detecção e diagnóstico dessa doença. Este trabalho propõe uma metodologia para a classificação, através de imagens de tomografias computadorizadas, de candidatos a nódulos pulmonares e candidatos a não-nódulos. O banco de imagens Lung Image Database Consortium (LIDC) é utilizado para a criação de uma base de imagens de candidatos a nódulos e uma base de imagens de candidatos a não-nódulos. Três técnicas são utilizadas para a extração de medidas de textura. A primeira delas é o algoritmo de vidas artificiais Artificial Crawlers. A segunda técnica é a utilização do Rose Diagram para a extração de medidas direcionais. A terceira e última técnica é um modelo híbrido que une as medidas do Artificial Crawlers e do Rose Diagram. Para a classificação é utilizado o classificador Máquina de Vetor de Suporte (MVS), com o kernel de base radial. Os resultados alcançados são muito promissores. Utilizando 833 exames do LIDC divididos em 60% para treino e 40% para teste, alcançou-se uma média de acurácia de 94,30%, média de sensibilidade de 91,86%, média de especificidade de 94,78%, coeficiente de variância da acurácia de 1,61% e área média das curvas ROC de 0,922.
PODDAR, KRITI. "IMPROVED LUNG PATTERN CLASSIFICATION FOR INTERSTITIAL LUNG DISEASE USING DEEP LEARNING". Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16845.
Texto completoChen, Pu. "Classification tree models for predicting cancer status". 2009. http://digital.library.duq.edu/u?/etd,109505.
Texto completoLibros sobre el tema "LUNG PATTERN CLASSIFICATION"
Denton, Christopher P. y Pia Moinzadeh. Systemic sclerosis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0121.
Texto completoCapítulos de libros sobre el tema "LUNG PATTERN CLASSIFICATION"
Wong, James S. J. y Tatjana Zrimec. "Classification of Lung Disease Pattern Using Seeded Region Growing". En Lecture Notes in Computer Science, 233–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_27.
Texto completoBobadilla, Julio Cesar Mendoza y Helio Pedrini. "Lung Nodule Classification Based on Deep Convolutional Neural Networks". En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 117–24. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52277-7_15.
Texto completoBlatt, Rossella, Andrea Bonarini y Matteo Matteucci. "Pattern Classification Techniques for Lung Cancer Diagnosis by an Electronic Nose". En Computational Intelligence in Healthcare 4, 397–423. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14464-6_18.
Texto completoRamos, Bernardo, Tania Pereira, Francisco Silva, José Luis Costa y Hélder P. Oliveira. "Differential Gene Expression Analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification". En Pattern Recognition and Image Analysis, 182–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_15.
Texto completoCardoso, Isadora, Eliana Almeida, Héctor Allende-Cid, Alejandro C. Frery, Rangaraj M. Rangayyan, Paulo M. Azevedo-Marques y Heitor S. Ramos. "Evaluation of Deep Feedforward Neural Networks for Classification of Diffuse Lung Diseases". En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 152–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75193-1_19.
Texto completoNascimento, Leonardo Barros, Anselmo Cardoso de Paiva y Aristófanes Corrêa Silva. "Lung Nodules Classification in CT Images Using Shannon and Simpson Diversity Indices and SVM". En Machine Learning and Data Mining in Pattern Recognition, 454–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31537-4_36.
Texto completoAlsubaie, Najah, Muhammad Shaban, David Snead, Ali Khurram y Nasir Rajpoot. "A Multi-resolution Deep Learning Framework for Lung Adenocarcinoma Growth Pattern Classification". En Communications in Computer and Information Science, 3–11. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95921-4_1.
Texto completoda Silva, Cleriston Araujo, Aristófanes Corrêa Silva, Stelmo Magalhães Barros Netto, Anselmo Cardoso de Paiva, Geraldo Braz Junior y Rodolfo Acatauassú Nunes. "Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM". En Machine Learning and Data Mining in Pattern Recognition, 810–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_61.
Texto completoVo, Kiet T. y Arcot Sowmya. "Directional Multi-scale Modeling of High-Resolution Computed Tomography (HRCT) Lung Images for Diffuse Lung Disease Classification". En Computer Analysis of Images and Patterns, 663–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_81.
Texto completoSørensen, Lauge, Saher B. Shaker y Marleen de Bruijne. "Texture Classification in Lung CT Using Local Binary Patterns". En Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 934–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_111.
Texto completoActas de conferencias sobre el tema "LUNG PATTERN CLASSIFICATION"
He, Jing Selena, Meng Han, Lei Yu y Chao Mei. "Lung Pattern Classification Via DCNN". En 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378090.
Texto completoPriya, C. Lakshmi, D. Gowthami y S. Poonguzhali. "Lung pattern classification for interstitial lung diseases using an ANN-back propagation network". En 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2017. http://dx.doi.org/10.1109/iccsp.2017.8286732.
Texto completoZahari, Rahimi, Julie Cox y Boguslaw Obara. "Quantifying the Uncertainty in 3D CT Lung Cancer Images Classification". En 2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS). IEEE, 2023. http://dx.doi.org/10.1109/icprs58416.2023.10179053.
Texto completoLatifi, Seyed Amir, Hassan Ghassemian y Maryam Imani. "Feature Extraction and Classification of Respiratory Sound and Lung Diseases". En 2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE, 2023. http://dx.doi.org/10.1109/ipria59240.2023.10147191.
Texto completoTuba, Eva, Ivana Strumberger, Nebojsa Bacanin y Milan Tuba. "Analysis of local binary pattern for emphysema classification in lung CT image". En 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2019. http://dx.doi.org/10.1109/ecai46879.2019.9042056.
Texto completoNasrullah, Nasrullah, Jun Sang, Mohammad S. Alam y Hong Xiang. "Automated detection and classification for early stage lung cancer on CT images using deep learning". En Pattern Recognition and Tracking XXX, editado por Mohammad S. Alam. SPIE, 2019. http://dx.doi.org/10.1117/12.2520333.
Texto completoShea, Daniel E., Sourabh Kulhare, Rachel Millin, Zohreh Laverriere, Courosh Mehanian, Charles B. Delahunt, Dipayan Banik et al. "Deep Learning Video Classification of Lung Ultrasound Features Associated with Pneumonia". En 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00312.
Texto completoDandil, Emre, Murat Cakiroglu, Ziya Eksi, Murat Ozkan, Ozlem Kar Kurt y Arzu Canan. "Artificial neural network-based classification system for lung nodules on computed tomography scans". En 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2014. http://dx.doi.org/10.1109/socpar.2014.7008037.
Texto completoMing, Joel Than Chia, Norliza Mohd Noor, Omar Mohd Rijal, Rosminah M. Kassim y Ashari Yunus. "Lung disease severity classification using reticular pattern and five class features based on GLCM". En TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8227860.
Texto completoGan, Bin, Chun-Hou Zheng y Hong-Qiang Wang. "A survey of pattern classification-based methods for predicting survival time of lung cancer patients". En 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999296.
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