{"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/classification-of-brain-hemorrhage-using-deep","title":"Classification of Brain Hemorrhage Using Deep Learning from CT Scan Images","arxiv_id":null,"date":"2023-01-26","proceeding":"International Conference on Information and Communication Technology for Development 2023 1","authors":["Nipa Anjum","Abu Noman Md. Sakib","Sk. Md. Masudul Ahsan"],"abstract":"Brain hemorrhage is a life-threatening problem that happens by bleeding inside human head. In this study, computed tomography (CT) scan images have been used to classify whether the case is hemorrhage or non-hemorrhage. Different convolutional neural network (CNN) models have been observed along with some pre-trained deep learning models such as VGG16, VGG19, ResNet150, ResNet152 and InceptionV3. Pre-trained models have performed well on the dataset but all of them are heavyweight architectures in terms of number of total parameters. But the proposed model is a lightweight architecture as well as a well performing one. After evaluating the model performance, it has been observed that the proposed model gave 96.67% accuracy, 97.08% sensitivity and 96.25% specificity which is the best among other custom CNN models.","url_abs":"https://doi.org/10.1007/978-981-19-7528-8_15","url_pdf":"https://doi.org/10.1007/978-981-19-7528-8_15","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":"classification-of-brain-hemorrhage-using-deep","repo_url":"https://github.com/anmspro/Classification-of-Brain-Hemorrhage-Using-Deep-Learning-from-CT-Scan-Images","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}