Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

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.

Detection of Breast Cancer in Mammogram Images Using Multi Attention Feature Extraction with Hybrid RSA Based AlexNet

“…In their study, Pati et al [15] created a deep transfer learning (DTL) model-based autonomous system for identifying breast cancer. For their investigation, they used mammography pictures from the publicly available online archive the cancer imaging archive (TCIA).…”

“…where, 𝑋( 𝑚, 𝑛) is derived by adapting the negative numbers to zero and gives the identical number again on obtaining any affirmative result which is demonstrated in Eq. (15). Inclusion this RELU layer into the design that was proposed as deep CNNs obtain substantially faster pace when integrating this specific layer (RELU).…”

En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis

“…The main objective of the SVM [38,39] is to bring a hyperplane for maximizing the margin between the existing dataset classes. The hyperplane can be defined by using equation 22.…”

DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction

“…Consequently, the price of health care amenities is drastically decreased [ 27 ]. Moreover, the framework allowed by cloud computing efficiently monitors patients affected by mosquito-borne diseases and seamlessly uses the medical records across clinics for efficient administration of health statistics [ 28 , 29 ]. Managing enormous amounts of data on the cloud, however, is extraordinarily difficult, and it slows the transmission across the internet, which can have dire effects such as endangering the lives of patients [ 30 ].…”