{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:28:45Z","timestamp":1776086925502,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T00:00:00Z","timestamp":1621900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Princess Nourah bint Abdulrahman University","award":["Fast-track Research Funding Program to support publication in the top journal"],"award-info":[{"award-number":["Fast-track Research Funding Program to support publication in the top journal"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2\u00b11.5% with average sensitivity and specificity of 87.5\u00b12.3% and 90.9\u00b11.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2\u00b11.3% with sensitivity and specificity of 91.7\u00b11.7% and 90.1\u00b12.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.<\/jats:p>","DOI":"10.3390\/s21113664","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T22:02:23Z","timestamp":1621980143000},"page":"3664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Precise Identification of Prostate Cancer from DWI Using Transfer Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Islam R.","family":"Abdelmaksoud","sequence":"first","affiliation":[{"name":"Bioengineering Department, University of Louisville, Louisville, KY 40292, USA"},{"name":"Faculty of Computers and Information, Mansoura University, Dakahlia 35516, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6291-7998","authenticated-orcid":false,"given":"Ahmed","family":"Shalaby","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of Louisville, Louisville, KY 40292, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2557-9699","authenticated-orcid":false,"given":"Ali","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of Louisville, Louisville, KY 40292, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2504-6051","authenticated-orcid":false,"given":"Mohammed","family":"Elmogy","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, Mansoura University, Dakahlia 35516, Egypt"}]},{"given":"Ahmed","family":"Aboelfetouh","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, Mansoura University, Dakahlia 35516, Egypt"}]},{"given":"Mohamed","family":"Abou El-Ghar","sequence":"additional","affiliation":[{"name":"Radiology Department, Urology and Nephrology Center, University of Mansoura, Dakahlia 35516, Egypt"}]},{"given":"Moumen","family":"El-Melegy","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Assiut University, Assiut 71515, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6421-6001","authenticated-orcid":false,"given":"Norah Saleh","family":"Alghamdi","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7264-1323","authenticated-orcid":false,"given":"Ayman","family":"El-Baz","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of Louisville, Louisville, KY 40292, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,25]]},"reference":[{"key":"ref_1","unstructured":"American Cancer Society (2020, February 10). 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