Citation: Pati, A.; Parhi, M.; Pattanayak, B.K.; Singh, D.; Singh, V.; Kadry, S.; Nam, Y.; Kang, B.-G. Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing. Diagnostics 2023, 13, 2191. https://doi.org/10.3390/ diagnostics13132191 Academic Editor: Henk A. Marquering Received: 8 May 2023 Revised: 18 June 2023 Accepted: 19 June 2023 Published: 27 June 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diagnostics Article Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing Abhilash Pati 1, * , Manoranjan Parhi 2 , Binod Kumar Pattanayak 1 , Debabrata Singh 3 , Vijendra Singh 4 , Seifedine Kadry 5,6,7,8 , Yunyoung Nam 9, * and Byeong-Gwon Kang 9, * 1 Department of Computer Science and Engineering, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India; binodpattanayak@soa.ac.in 2 Centre for Data Sciences, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India; manoranjanparhi@soa.ac.in 3 Department of Computer Applications, Faculty of Engineering and Technology (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India; debabratasingh@soa.ac.in 4 School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India; vijendra.singh@ddn.upes.ac.in 5 Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway; skadry@gmail.com 6 Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates 7 Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon 8 MEU Research Unit, Middle East University, Amman 11831, Jordan 9 Department of ICT Convergence, Soonchunhyang University, Asan 31538, Republic of Korea * Correspondence: er.abhilash.pati@gmail.com (A.P.); ynam@sch.ac.kr (Y.N.); bgkang@sch.ac.kr (B.-G.K.) Abstract: Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output. Keywords: breast cancer diagnosis; Fog computing; IoT; convolutional neural network (CNN); deep transfer learning (DTL) 1. Introduction Breast cancer is the most frequent form of cancer in women and is responsible for the deaths of approximately 36% of all women annually. Among both sexes, breast cancer has the second-highest incidence and fatality rates [1–3]. According to the World Health Organization (WHO), breast cancer is the second leading cause of death in women globally. Diagnostics 2023, 13, 2191. https://doi.org/10.3390/diagnostics13132191 https://www.mdpi.com/journal/diagnostics