A novel deep learning architecture for multiple sclerosis Diagnosis : Gau-U-Net /
بنية جديدة للتعلم العميق لتشخيص التصلب المتعدد : Gau-U-Net /
by Roba Gamal Mohamed ; Under the Supervision of Prof. Hoda Baraka, Assoc. Prof. Mayada Mansour.
- 96 pages : illustrations ; 30 cm. + CD.
Thesis (M.Sc.)-Cairo University, 2023.
Bibliography: pages 66-69.
Multiple sclerosis is an autoimmune disease that affects the brain and nervous system. 2.8 million people are estimated to live with MS worldwide (35.9 per 100,000 population). The pooled incidence rate across 75 reporting countries is 2.1 per 100,000 persons per year, and the mean age of diagnosis is 32 years. Lesions resulting from the disease can be spotted in the patient’s MRI scans. In this paper, a novel Deep learning architecture, GAU-U-net, is proposed. The model is inspired by the very famous U-Net architecture used for semantic segmentation and is frequently employed in the segmentation of medical images. The proposed model consists of a 3D U-Net after adding a new attention technique inspired by the Global Attention Upsample (GAU) unit. After using GAU-unet architecture, the Dice coefficient increased from 64% to 72% compared to 3D-Unet.Also, compared with the Unet attention network, the dice coefficient increased from 69% to around 72%, with a considerable decline in the number of model parameters in favor of our architecture, which uses 28M parameters compared to Unet-attention, which employs 100 M parameters ﯾﺘﺄﺛﺮ اﻟﺪﻣﺎغ واﻟﺤﺒﻞ اﻟﺸﻮﻛﻲ ﺑﻤﺮض اﻟﺘﺼﻠﺐ اﻟﻤﺘﻌﺪد، وھﻮ ﻣﻦ اﺿﻄﺮاﺑﺎت اﻟﻤﻨﺎﻋﺔ اﻟﺬاﺗﯿﺔ. وﻋﺎدة ﻣﺎ ﯾﺘﻢ ﺗﺸﺨﯿﺼﮫ ﻣﻦ ﺧﻼل اﻟﺘﺼﻮﯾﺮ ﺑﺎﻟﺮﻧﯿﻦ اﻟﻤﻐﻨﺎطﯿﺴﻲ.ﯾﺘﻄﻮر اﻟﻤﺮض إﻟﻰ آﻓﺎت ﺑﯿﻀﺎء ﯾﻤﻜﻦ أن ﺗﻜﻮن واﺿﺤﺔ ﻓﻲ اﻟﺘﺼﻮﯾﺮ ﺑﺎﻟﺮﻧﯿﻦ اﻟﻤﻐﻨﺎطﯿﺴﻲ وﯾﻤﻜﻦ ﺗﻘﺴﯿﻢ اﻵﻓﺎت ﯾﺪوﯾًﺎ ﺑﻮاﺳﻄﺔ اﻟﺨﺒﺮاء، وھﺬا ﯾﺴﺘﻐﺮق وﻗﺘًﺎ طﻮﯾﻼً ﻟﻠﻐﺎﯾﺔ.إن ﺗﻄﺒﯿﻖ ﺗﻘﻨﯿﺎت اﻟﺮؤﯾﺔ اﻟﺤﺎﺳﻮﺑﯿﺔ ﻟﺘﺠﺰﺋﺔ اﻵﻓﺎت ﯾﻤﻜﻦ أن ﯾﺠﻌﻞ اﻟﻌﻤﻠﯿﺔ أﺳﮭﻞ وأﺳﺮع، ﻓﻲ ھﺬه