MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02865cam a2200325 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
EG-GiCUC |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
211009s2021 ua d f m 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
EG-GiCUC |
| Language of cataloging |
eng |
| Transcribing agency |
EG-GiCUC |
| 041 0# - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 049 ## - LOCAL HOLDINGS (OCLC) |
|---|
| Holding library |
Deposite |
| 097 ## - Thesis Degree |
|---|
| Thesis Level |
M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
|---|
| Classification number |
Cai01.20.01.M.Sc.2021.Ay.D |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Aya Allah Adel Ahmed Mohamed |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Deep learning for medical image diagnosis / |
| Statement of responsibility, etc. |
Aya Allah Adel Ahmed Mohamed ; Supervised Khaled Mostafa , Mona Mohamed Soliman , Nour Eldeen M. Khalifa |
| 246 15 - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
التعلم العميق لتشخيص الصور الطبية |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Cairo : |
| Name of publisher, distributor, etc. |
Aya Allah Adel Ahmed Mohamed , |
| Date of publication, distribution, etc. |
2021 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
83 Leaves : |
| Other physical details |
charts ; |
| Dimensions |
30cm |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Information Technology |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
One of the most important tasks while developing medical diagnosis software system is diseases prediction. Artificial intelligence and neural networks are two main methods that have already been used to solve medical diagnosis problems. Deep Learning strategies have recently been popular in a variety of applications, including assisting in medical diagnosis. Patients can analyse disease based on clinical and laboratory symptoms with sufficient data and get a more efficient outcome for a particular disease in a very simple and timely manner. DL enhances the performance for medical image diagnosis by generating features directly from raw images. DL is a data-driven approach, it highly depends on the data used. Data limitation is always a critical problem when designing a DL model. This thesis provides a solution for medical image diagnosis with a limited number of medical images by proposing two different diagnosis models.The first one is a transfer learning-based model with a hinge loss function instead of the traditional softmax function. This diagnosis model for medical image classification utilizes the use of two different scenarios based on Inception V3 and Xception architectures.The second model utilizes the concept of ensemble learning by introducing an end-toend ensemble model. This proposed model is dependent on three pre-trained Convolutional Neural Network (CNN) (e.g. Xception, Inception, and VGG19 models). More layers were added to allow this model to discover the best concatenation weights among all three models. Both models are used for retinal diseases diagnosis and Alzheimer disease diagnosis |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Issued also as CD |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Convolutional Neural Network (CNN) |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
DL model |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Medical image diagnosis |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Khaled Mostafa , |
| Relator term |
|
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mona Mohamed Soliman , |
| Relator term |
|
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Nour Eldeen M. Khalifa , |
| Relator term |
|
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| Cataloger |
Nazla |
| Reviser |
Revisor |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| Cataloger |
Shimaa |
| Reviser |
Cataloger |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
