Discussion of the master\u2019s thesis in the College of Computer Science and Mathematics -Department of software : ” Brain Tumor Images Segmentation\u00a0Using Artificial Intelligence Techniques ”<\/p>\n
Master\u2019s thesis by Mohanad Raied Ibrahim Saied<\/p>\n
supervised by \u00a0\u00a0Dr. Jamal Salahaldeen Majeed Sayed Hamo<\/p>\n
The primary objective of this project is to work on a system for constructing a segmentation system for the tumor area in the human brain and detection the type of tumor based on magnetic resonance images using artificial intelligence techniques, which helps the oncologist in the accuracy and speed of diagnosis of the patient\u2019s condition. Multi-model deep learning was used to build two automatic segmentation models through the automatic segmentation algorithm (U-Net) and a model for diagnosing the type of tumor using the algorithm (Efficent Net-B3) from among the most common types, which are (glioma brain tumor, meningioma brain tumor, pituitary tumor), as well as diagnosing the brain in its healthy state. High-accuracy results were obtained compared to previous studies.<\/p>\n
<\/p>\n
The discussion committee consists of<\/p>\n
(Chairman) Asst. Prof. Dr. Yusra Faisal Mohammed .<\/p>\n
(Member) Asst. Prof. Dr. Alaa Yaseen Taha .<\/p>\n
(Member) Dr. Shatha Abdullah Mohammed .<\/p>\n
(member and supervisor) Asst. Prof. Dr. Jamal Salahaldeen Majeed Sayed Hamo<\/p>\n","protected":false},"excerpt":{"rendered":"
Discussion of the master\u2019s thesis in the College of Computer Science and Mathematics -Department of software : ” Brain Tumor Images Segmentation\u00a0Using Artificial Intelligence Techniques ” Master\u2019s thesis by Mohanad Raied Ibrahim Saied supervised by \u00a0\u00a0Dr. Jamal Salahaldeen Majeed Sayed Hamo The primary objective of this project is to work on a system for constructing a segmentation system for the tumor area in the human brain and detection the type of tumor based on magnetic resonance images using artificial intelligence techniques, which helps the oncologist in the accuracy and speed of diagnosis of the patient\u2019s condition. Multi-model deep learning was used [Read More]<\/a><\/p>\n","protected":false},"author":24,"featured_media":39365,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-39364","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-viva"],"_links":{"self":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/39364","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/users\/24"}],"replies":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/comments?post=39364"}],"version-history":[{"count":1,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/39364\/revisions"}],"predecessor-version":[{"id":39366,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/39364\/revisions\/39366"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/media\/39365"}],"wp:attachment":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/media?parent=39364"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/categories?post=39364"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/tags?post=39364"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}