Computer-Aided Multi-Label Retinopathy Diagnosis Via Inter-Disease Graph Regularization / (Record no. 170833)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05272namaa22004091i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20250223033433.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250213s2024 |||a|||fr|m|| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610.28 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 610.28 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.13.03.M.Sc.2024.Ta.C |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Tasnim Samir Mohamed Elsayed, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Computer-Aided Multi-Label Retinopathy Diagnosis Via Inter-Disease Graph Regularization / |
| Statement of responsibility, etc. | by Tasnim Samir Mohamed Elsayed ; Under the Supervision of Dr. Muhammad Ali Rushdi |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | التشخيص متعدد التصنيفات المعضد بالحاسوب لأمراض شبكية العين باستخدام مخططات الارتباط بين الأمراض / |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 88 pages : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc.)-Cairo University, 2024. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 74-88. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Computer-aided diagnosis (CAD) of retinal fundus diseases is crucial for effective<br/>treatment planning and avoidance of vision deterioration and loss. Most existing CAD<br/>systems are focused on learning to differentiate between retinal fundus diseases,<br/>assuming that diseases are independent and ignoring disease co-occurrences. In this<br/>thesis, we address this limitation in multi-label classification of retinal fundus diseases<br/>and introduce an end-to-end deep learning framework that accounts for label<br/>relationships via graph-theoretic regularization. Specifically, we trained a convolutional<br/>neural network for multi-label retinal disease classification. The training process for<br/>this network embeds the graph prior in a scalable neighbor discriminative loss with<br/>binary cross entropy (SNDL-BCE). The proposed model was validated through<br/>extensive experiments on the retinal fundus multi-disease image dataset (RFMiD). The<br/>model successfully detected the disease risk with area under the curve (AUC) of<br/>95.02% on the validation set and 95.8% on the test set. Furthermore, the model<br/>classified 28 different retinal fundus diseases with multi-disease score metric of 74.68%<br/>on the validation set and 73.99% on the test set. Overall, the model demonstrated a<br/>competitive performance with a final score of 84.85% on the validation set and 85% on<br/>the test set. Also, the model achieved an F1-score of 77.16% on the test data. In<br/>addition, gradient-weighted class activation map (Grad-CAM) visualization exhibited<br/>high explainability and plausibility for the outcomes of our model. Moreover, our<br/>model compares well with other state-of-the-art methods for retinal disease<br/>classification. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | تشخيص أمراض قاع الشبكية بمساعدة الحاسوب أمر بالغ الأهمية لتخطيط العلاج الفعال وتجنب تدهور الرؤية وفقدانها. أغلب الأنظمة الحالية للتشخيص بالحاسوب تركز على تشخيص كل مرض شبكي بشكل مستقل، بينما تتجاهل علاقات ارتباط الأمراض ببعضها البعض. في هذه الرسالة، نناقش مشكلة التشخيص متعدد التصنيفات لأمراض شبكية قاع العين ونقدم إطارًا شاملًا باستخدام التعلم العميق أخذًا في الحسبان العلاقات بين الأمراض من خلال مخططات الارتباط بين الأمراض. على وجه التحديد، قمنا بتدريب شبكات عصبية تلافيفية لعمل تصنيف متعدد لحالات أمراض الشبكية. يتضمن تدريب هذه الشبكات مخططات الارتباط بين الأمراض المعلومة مُسبقًا والتى يتم دمجها فى دالة الفقد التمييزية القابلة للتطوير المعتمدة على الجيران مع دالة العشوائية الثنائية. تم التحقق من صحة النموذج المقترح من خلال تجارب مكثفة على مجموعة بيانات (RFMID2021). النموذج المقترح نجح في تحديد وجود المرض وتصنيف 28 مرضاً مختلفًا من أمراض شبكية قاع العين بدرجة إجمالية 85٪ في مجموعة الاختبار. أظهر نموذجنا قابلية عالية لتفسير النتائج ومعقوليتها مقارنةً مع أحدث الأساليب لتصنيف أمراض الشبكية. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issued also as CD |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Biomedical Engineering and Systems |
| Source of heading or term | qrmak |
| 653 #0 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Multi-label classification |
| -- | Graph regularization |
| -- | Computer-aided diagnosis |
| -- | Retinopathy |
| -- | Deep learning |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Muhammad Ali Rushdi |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2024 |
| Supervisory body | Muhammad Ali Rushdi |
| Discussion body | Manal Abdel Wahed |
| -- | Mohamed Nagy Saad Elziftawy |
| Universities | Cairo University |
| Faculties | Faculty of Engineering |
| Department | Department of Biomedical Engineering and Systems |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Eman Ghareeb |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 13.02.2025 | 90522 | Cai01.13.03.M.Sc.2024.Ta.C | 01010110090522000 | 13.02.2025 | 13.02.2025 | Thesis |