MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03008cam a2200313 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
EG-GiCUC |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
210112s2020 ua dh 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 |
Cai01.18.02.M.Sc.2020.Sa.E |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sarah Ali Abdelaziz Ismael |
| 245 13 - TITLE STATEMENT |
|---|
| Title |
An enhanced deep learning approach for cancer diagnosis / |
| Statement of responsibility, etc. |
Sarah Ali Abdelaziz Ismael ; Supervised Hesham A. Hefny , Ammar Mohammed |
| 246 15 - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
أسلوب مجود فى التعليم العميق لتشخيص السرطان |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Cairo : |
| Name of publisher, distributor, etc. |
Sarah Ali Abdelaziz Ismael , |
| Date of publication, distribution, etc. |
2020 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
87 P . : |
| Other physical details |
charts , facsmilies ; |
| Dimensions |
30cm |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Computer and Information Science |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help saving the patient{u2019}s life. Human diagnosis based on MRI images is an unreliable, error-prone, time-consuming, highly specialized task that depends on the experience of the radiologist. Misdiagnosis of brain tumor type can prevent the effective response to medical treatment and decrease the chance of survival among patients. Therefore, there is a high need in the Artificial Intelligence field for a highly accurate Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Traditional machine learning approaches required feature extraction which is a manual and time-consuming task that requires prior knowledge about the problem domain. Over recent years, deep learning has shown an optimistic performance in computer vision systems in the medical imaging domain In this thesis, we propose an enhanced approach for classifying brain tumor types using Residual Networks. The proposed model is evaluated on a benchmark dataset containing 3064 MRI images of 3 brain tumor types. We have achieved the highest accuracy of 99% outperforming the other previous work. The results were evaluated under several performance metrics such as accuracy, precision, recall, f1-score, and balanced accuracy. Furthermore, the results have been evaluated on two different distributions of the dataset, an image-level approach, where images were split randomly into training and validation sets. And a patientlevel approach where a patient is present in either in the training set or validation set but not both |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Issued also as CD |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Artificial Neural Network |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Convolutional Neural Network |
| 653 #4 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Machine Learning |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ammar Mohammed , |
| Relator term |
|
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hesham A. Hefny , |
| Relator term |
|
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| Cataloger |
Amira |
| Reviser |
Cataloger |
| 905 ## - LOCAL DATA ELEMENT E, LDE (RLIN) |
|---|
| Cataloger |
Nazla |
| Reviser |
Revisor |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
