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

Nasir Ayub; Turki Alghamdi; Irfanud Din; Arshad Ali; Hamayun Khan; Oqila Ganiyeva; Samariddin Makhmudov

doi:10.48084/etasr.11403

Authors

Nasir Ayub Department of Computer Science, Faculty of Computer Science & IT, Superior University, Lahore, Pakistan
Turki Alghamdi Faculty of Computer and Information Systems, Islamic University of Madinah, Saudi Arabia
Irfanud Din Department of Software Engineering, New Uzbekistan University, Tashkent, Uzbekistan
Arshad Ali Faculty of Computer and Information Systems, Islamic University of Madinah, Saudi Arabia
Hamayun Khan Department of Computer Science, Faculty of Computer Science & IT, Superior University, Lahore, Pakistan
Oqila Ganiyeva Department of Information Technology in Mathematics and Education, Tashkent State Pedagogical University, Tashkent, Uzbekistan
Samariddin Makhmudov Department of Finance and Tourism, Termez University of Economics and Service, Termez Uzbekistan | Department of Economics, Mamun University, Khiva, Uzbekistan

Volume: 15 | Issue: 6 | Pages: 30612-30616 | December 2025 | https://doi.org/10.48084/etasr.11403

Received: 30 April 2025 | Revised: 12 May 2025, 28 May 2025, and 1 June 2025 | Accepted: 6 June 2025 | Online: 18 November 2025

Corresponding author: Irfanud Din

Abstract

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.

Keywords:

breast cancer classification, Medical Internet of Things (MioT), deep learning, Convolutional Neural Network (CNN)

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