An Enhanced Artificial Intelligence and Deep Learning Assisted Breast Cancer Classification and Diagnosis Based on the Internet of Medical Things (IOMTs)

Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. An effective early diagnosis of breast cancer can lead to better patient outcomes and improved treatment success. In recent years, IoT technology, combined with Artificial Intelligence and Machine Learning (ML) methods, has completely revolutionized modern medical diagnostics. The combination of these novel system elements enables faster and better diagnoses through their ability to process data. Many people continue to die from breast cancer because the present diagnostic methods fail to detect the disease at its initial stages, despite significant advances in medical science. This study presents an IoT-based medical diagnostic system that distinguishes between patients who have tumors and those who do not. The proposed model performs tumor versus non-tumor identification with a 95% accuracy rate through a CNN with optimized hyperparameters. Medical staff can use this technology to enhance the accuracy of breast cancer diagnosis through the combination of medical devices with AI applications and healthcare infrastructure. This approach has the potential to reduce breast cancer deaths through its effective early detection, which could be established in the long term. Medical IoT technology, along with continuous innovation, can transform healthcare delivery while improving worldwide patient outcomes.