A combined deep learning-regression paradigm for echocardiography-based left ventricle ejection fraction prediction / (Record no. 174695)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04486namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20251019112405.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251011s2025 ua a|||frm||| 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.2025.Ra.C |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Rahma Sayed Saad Elsayed, |
| Preparation | preparation. |
| 245 12 - TITLE STATEMENT | |
| Title | A combined deep learning-regression paradigm for echocardiography-based left ventricle ejection fraction prediction / |
| Statement of responsibility, etc. | by Rahma Sayed Saad Elsayed ; Supervisors Prof. Manal Abdel Wahed, Prof. Neven Saleh. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | نموذج مركب للتعلم العميق والانحدار لتوقع كسر القذف للبطين الأيسر المعتمد على تخطيط صدى القلب |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 64 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, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 61-64. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | With an emphasis on apical four-chamber views, this study improves cardiac function categorization and LVEF prediction from echocardiographic images using deep learning, machine learning, and regression techniques. Advanced convolutional neural networks were used to optimize feature representation, with ResNet-50 demonstrating the highest classification accuracy. The comprehensive feature representations were then integrated into both the Gaussian machine learning model and the Gaussian Process Regression model. For classification, the model achieved 89% accuracy and was then validated using a new dataset, achieving 87.88% accuracy. The regression model was constructed to predict ejection fraction values, yielding a high R-squared value of 0.92 and a high mean absolute error (MAE) of 1.32, and for the new dataset, the R-squared value of 0.88 and a high mean absolute error (MAE) of 3.563. The results underscore the effectiveness of enhanced feature extraction in advancing cardiac function assessment and addressing gaps in the literature. |
| 520 #3 - SUMMARY, ETC. | |
| Summary, etc. | مع التركيز على مشاهد الأربع غرف القمية، تحسن هذه الدراسة تصنيف وظيفة القلب وتنبؤ كسر القذف البطيني الأيسر من صور الإيكو باستخدام تقنيات التعلم العميق، التعلم الآلي، وتقنيات الانحدار. تم استخدام الشبكات العصبية الالتفافية المتقدمة لتحسين تمثيل الميزات، حيث أظهر نموذج ResNet-50 أعلى دقة في التصنيف. تم دمج تمثيلات الميزات الشاملة في كل من نموذج التعلم الآلي باستخدام عملية جاوسية (Gaussian) ونموذج الانحدار باستخدام عملية جاوسية. في التصنيف، حقق النموذج دقة بلغت 89%، ثم تم التحقق من صحته باستخدام مجموعة بيانات جديدة، حيث تم الوصول إلى دقة 87.88%. تم بناء نموذج انحدار للتنبؤ بقيم كسر القذف، مما أسفر عن قيمة عالية لمعامل التحديد (R²) بلغت 0.92 وخطأ مطلق متوسط (MAE) قدره 1.32. أما بالنسبة لمجموعة البيانات الجديدة، فكانت قيمة R² تساوي 0.88، مع خطأ مطلق متوسط (MAE) قدره 3.563. تؤكد النتائج فعالية تحسين استخراج الميزات في تحسين تقييم وظيفة القلب ومعالجة الفجوات في الادبيات. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issues also as CD. |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Biomedical Engineering |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | الهندسة الحيوية الطبية |
| 653 #1 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Echocardiography |
| -- | Left ventricle ejection fraction |
| -- | Deep learning |
| -- | Machine learning |
| -- | Regression model |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Manal Abdel Wahed |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Neven Saleh |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2025 |
| Supervisory body | Manal Abdel Wahed |
| -- | Neven Saleh |
| Discussion body | Ahmed Hisham Kandil |
| -- | Khaled Mostafa El Sayed |
| Universities | Cairo University |
| Faculties | Faculty of Engineering |
| Department | Department of Biomedical Engineering and Systems |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Shimaa |
| Reviser Names | Eman Ghareb |
| 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 | 11.10.2025 | 92128 | Cai01.13.03.M.Sc.2025.Ra.C | 01010110092128000 | 11.10.2025 | 11.10.2025 | Thesis |