{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,13]],"date-time":"2025-04-13T11:04:53Z","timestamp":1744542293632,"version":"3.37.3"},"reference-count":61,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients\u2019 recoveries. By using IoMT to diagnose and examine BreakHis v1 400\u00d7 breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model \u201cMobileNet-SVM\u201d, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400\u00d7 BCH images is presented. When tested against a real dataset of BreakHis v1 400\u00d7 BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.<\/jats:p>","DOI":"10.3390\/s23020656","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T08:54:49Z","timestamp":1672995289000},"page":"656","source":"Crossref","is-referenced-by-count":34,"title":["MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2592-2824","authenticated-orcid":false,"given":"Roseline Oluwaseun","family":"Ogundokun","sequence":"first","affiliation":[{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania"},{"name":"Department of Computer Science, Landmark University, Omu Aran 251103, Kwara, Nigeria"}]},{"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Communication, \u00d8stfold University College, 1757 Halden, Norway"}]},{"given":"Akinyemi Omololu","family":"Akinrotimi","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Augustine University Ilara-Epe, Lagos 106103, Lagos, Nigeria"}]},{"given":"Hasan","family":"Ogul","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Communication, \u00d8stfold University College, 1757 Halden, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"ref_1","unstructured":"(2020, November 08). 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