{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/brain-tumor-detection-using-convolutional","title":"Brain Tumor Detection using Convolutional Neural Network","arxiv_id":null,"date":"2019-05-03","proceeding":"international conference on advances in science, engineering and robotics technology (ICASERT) 2019 5","authors":["Tonmoy Hossain","Fairuz Shadmani Shishir","Mohsena Ashraf","MD Abdullah Al Nasim","Faisal Muhammad Shah"],"abstract":"Brain Tumor segmentation is one of the most\r\ncrucial and arduous tasks in the terrain of medical image\r\nprocessing as a human-assisted manual classification can result\r\nin inaccurate prediction and diagnosis. Moreover, it is an\r\naggravating task when there is a large amount of data present\r\nto be assisted. Brain tumors have high diversity in appearance\r\nand there is a similarity between tumor and normal tissues and\r\nthus the extraction of tumor regions from images becomes\r\nunyielding. In this paper, we proposed a method to extract brain\r\ntumor from 2D Magnetic Resonance brain Images (MRI) by\r\nFuzzy C-Means clustering algorithm which was followed by\r\ntraditional classifiers and convolutional neural network. The\r\nexperimental study was carried on a real-time dataset with\r\ndiverse tumor sizes, locations, shapes, and different image\r\nintensities. In traditional classifier part, we applied six\r\ntraditional classifiers namely Support Vector Machine (SVM),\r\nK-Nearest Neighbor (KNN), Multilayer Perceptron (MLP),\r\nLogistic Regression, Naïve Bayes and Random Forest which was\r\nimplemented in scikit-learn. Afterward, we moved on to\r\nConvolutional Neural Network (CNN) which is implemented\r\nusing Keras and Tensorflow because it yields to a better\r\nperformance than the traditional ones. In our work, CNN\r\ngained an accuracy of 97.87%, which is very compelling. The\r\nmain aim of this paper is to distinguish between normal and\r\nabnormal pixels, based on texture based and statistical based\r\nfeatures.","url_abs":"https://scholar.google.com/citations?view_op=view_citation&hl=en&user=zQKHA64AAAAJ&citation_for_view=zQKHA64AAAAJ:2osOgNQ5qMEC","url_pdf":"https://www.researchgate.net/profile/Md-Abdullah-Nasim/publication/337768246_Brain_Tumor_Detection_Using_Convolutional_Neural_Network/links/5eddf25a45851529454462b3/Brain-Tumor-Detection-Using-Convolutional-Neural-Network.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"brain-tumor-detection-using-convolutional","repo_url":"https://github.com/nasim-aust/Brain-Tumor-Segmentation-using-CNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"brain-tumor-detection-using-convolutional","repo_url":"https://github.com/nafiuny/Optimized_TumorClassifierResNetSD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}