A machine learning approach for diagnosing medical images of breast cancer / (Record no. 78671)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02900cam a2200349 a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EG-GiCUC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250223032626.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 201110s2020 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 | Cai01.20.03.M.Sc.2020.Wa.M |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Walid Saleh Mohsen Aldhabyani |
| 245 12 - TITLE STATEMENT | |
| Title | A machine learning approach for diagnosing medical images of breast cancer / |
| Statement of responsibility, etc. | Walid Saleh Mohsen Aldhabyani ; Supervised Aly Aly Fahmy , Hussein Khaled , Mohamed Gomaa |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | طريقة التعلم الآلى لتشخيص الصور الطبية لسرطان الثدى |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Cairo : |
| Name of publisher, distributor, etc. | Walid Saleh Mohsen Aldhabyani , |
| Date of publication, distribution, etc. | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 125 Leaves : |
| Other physical details | charts , facimiles ; |
| Dimensions | 30cm |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Computer Science |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Breast cancer is one of the most common and deadliest cancer for women worldwide. However, early detection increases the chances of survival to virtually 100%. Radiologists use ultrasound images of the breast to look for signs of tumor formation such as microcal- cifications and breast masses. We aim to detect these signs using convolutional networks, a modern machine learning model that performs image classification in a single learnable step. After testing different network architectures and training configurations, we showed that convolutional networks are able to classify breast cancer with promising results. Fur- thermore, this performance will only improve as richer data sets become available. We highly encourage research in this direction. Breast cancer classification and detection using ultrasound imaging are considered a significant step in computer-aided diagnosis systems.Over the previous decades, re- searchers have proved the opportunities to automate the initial tumor classification and detection.The shortage of popular datasets of ultrasound images of breast cancer prevents researchers to get a good performance of the classification algorithms. So, data augmen- tations are used to enlarge the dataset. However, traditional data augmentation approaches are firmly limited, especially in tasks where the images follow strict standards, as in the case of medical datasets. So, a data augmentation Generative Adversarial Network (GAN) is used beside traditional augmentation.Higher accuracies are achieved when merging both methods |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Additional physical form available note | Issued also as CD |
| 653 #4 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Breast cancer |
| 653 #4 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Diagnosing medical images |
| 653 #4 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Machine learning |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Aly Aly Fahmy , |
| Relator term | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Hussein Khaled , |
| Relator term | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mohamed Gomaa , |
| Relator term | |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://172.23.153.220/th.pdf">http://172.23.153.220/th.pdf</a> |
| 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 |
| Source of classification or shelving scheme | Not for loan | Home library | Current library | Date acquired | Full call number | Barcode | Date last seen | Koha item type | Copy number |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 11.02.2024 | Cai01.20.03.M.Sc.2020.Wa.M | 01010110081972000 | 22.09.2023 | Thesis | ||
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | مخـــزن الرســائل الجـــامعية - البدروم | 11.02.2024 | Cai01.20.03.M.Sc.2020.Wa.M | 01020110081972000 | 22.09.2023 | CD - Rom | 81972.CD |