An Efficient Optimization System for Early Breast Cancer Diagnosis based on Internet of Medical Things and Deep Learning

Abstract

Improving patient outcomes and treatment efficacy requires effective early detection of breast cancer. Recently, medical diagnostics has been transformed by merging the Internet of Things (IoT) technology with AI and ML methods. Better and faster diagnoses have been made possible by this revolutionary synergy, which allows the study of both real-time and historical data. Unfortunately, many people still die from breast cancer because modern diagnostics are not good enough to catch the disease in its early stages, even though medical science has come a long way. To overcome this obstacle, this study introduces a new medical diagnostic system that utilizes IoT to accurately distinguish between patients with and without tumors. The proposed model achieved 95% classification accuracy in differentiating between non-tumor and tumor instances by utilizing a Convolutional Neural Network (CNN) with hyperparameter adjustment. This approach can improve the accuracy and efficiency of breast cancer diagnosis by integrating medical devices with AI applications and healthcare infrastructure. In the long run, this study could help reduce breast cancer deaths by increasing early detection rates. This study can revolutionize healthcare delivery and improve patient outcomes on a global scale through continued innovation and collaboration with medical IoT technology.