Improving Breast Cancer Diagnosis with AI Mammogram Image Analysis
Tiruveedula Gopi Krishna, Mohamed Abdeldaiem Mahboub
Abstract
Cancer is one of the worst diseases ailing humanity, but there is no known cure. Breast cancer is one of the most prevalent types of disease. Women aged 20–59 are disproportionately affected by breast cancer; however, death rates can be reduced with proper screening and treatment. Breast cancer detection and classification into cancer and healthy cells is one of the disease's most intractable difficulties. Cancer and healthy cells can be differentiated using AI methods based on their form and other biological features. This study proposes an artificial intelligence (AI)-enabled heuristic framework (AI-HF) with the Internet of Medical Things to identify and classify breast cancer at its earliest stages through machine intelligence techniques. The proposed method identifies cancer cells by extracting shape and texture-based features and then classifying them using machine learning classification techniques. Furthermore, this paper uses support vector machines to diagnose cancer based on optimal features taken from images of segmented cells. Experimental results on real datasets show that the suggested method accurately detects and classifies abnormal cells in breast cancer images rate of 98%. Finally, our AI-HF classifier is an efficient model that, in contrast to traditional techniques, can rapidly identify between cancer cells and healthy cells based on attributes extracted from images of the cells. Received: 9 July 2024 | Revised: 19 August 2024 | Accepted: 11 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Tiruveedula Gopi Krishna: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing - review & editing, Visualization, Project administration. Mohamed Abdeldaiem Abdelhadi Mahboub: Resources, Data curation, Writing - original draft, Supervision.