IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

• Published in: IEEE access Vol. 9; pp. 146478 - 146491
• Main Authors: Siddiqui, Shahan Yamin, Haider, Amir, Ghazal, Taher M., Khan, Muhammad Adnan, Naseer, Iftikhar, Abbas, Sagheer, Rahman, Muhibur, Khan, Junaid Ahmad, Ahmad, Munir, Hasan, Mohammad Kamrul, A, Afifi Mohammed, Ateeq, Karamath
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological system modeling; Breast cancer; breast cancer prediction; convolutional neural network; Convolutional neural networks; Deep learning; Feature extraction; Health services; Internet of Medical Things; Mammography; Medical diagnostic imaging; Medical imaging; Model accuracy; Mortality; Solid modeling; System effectiveness
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3123472

Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. 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. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.