{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T01:30:25Z","timestamp":1791509425847,"version":"4.3.4"},"reference-count":54,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,9,22]]},"abstract":"<jats:p>Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework\u2019s efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection.<\/jats:p>","DOI":"10.1155\/2022\/1100775","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T15:37:23Z","timestamp":1663861043000},"page":"1-12","source":"Crossref","is-referenced-by-count":10,"title":["Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0101-0329","authenticated-orcid":true,"given":"Amjad Rehman","family":"Khan","sequence":"first","affiliation":[{"name":"Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3138-3801","authenticated-orcid":true,"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[{"name":"Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0078-7849","authenticated-orcid":true,"given":"Tariq","family":"Sadad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4424-5600","authenticated-orcid":true,"given":"Haitham","family":"Nobanee","sequence":"additional","affiliation":[{"name":"College of Business, Abu Dhabi University, Abu Dhabi, UAE"},{"name":"Oxford Center for Islamic Studies, University of Oxford, Oxford, UK"},{"name":"Faculty of Humanities & Social Sciences, University of Liverpool, Liverpool, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3406-4320","authenticated-orcid":true,"given":"Saeed Ali","family":"Bahaj","sequence":"additional","affiliation":[{"name":"MIS Department College of Business Administration, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104248"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11020241"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-018-4638-5"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-021-05489-8"},{"key":"5","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2021.3127862","article-title":"Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms","volume":"9","author":"A. 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