Transformative insights: Image-based breast cancer detection and severity assessment through advanced AI techniques

• Published in: Journal of intelligent systems 2024-10, Vol.33 (1), p.105-16
• Main Authors: Patra, Ankita, Biswas, Preesat, Behera, Santi Kumari, Barpanda, Nalini Kanta, Sethy, Prabira Kumar, Nanthaamornphong, Aziz
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Breast cancer; cancer diagnosis; Deep learning; detection; Diagnostic systems; Efficiency; Explainable artificial intelligence; Identification; Image processing; Machine learning; Medical diagnosis; Medical imaging; Neural networks; Paradigms; Pattern recognition
• ISSN: 2191-026X; 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2024-0172

In the realm of image-based breast cancer detection and severity assessment, this study delves into the revolutionary potential of sophisticated artificial intelligence (AI) techniques. By investigating image processing, machine learning (ML), and deep learning (DL), the research illuminates their combined impact on transforming breast cancer diagnosis. This integration offers insights into early identification and precise characterization of cancers. With a foundation in 125 research articles, this article presents a comprehensive overview of the current state of image-based breast cancer detection. Synthesizing the transformative role of AI, including image processing, ML, and DL, the review explores how these technologies collectively reshape the landscape of breast cancer diagnosis and severity assessment. An essential aspect highlighted is the synergy between advanced image processing methods and ML algorithms. This combination facilitates the automated examination of medical images, which is crucial for detecting minute anomalies indicative of breast cancer. The utilization of complex neural networks for feature extraction and pattern recognition in DL models further enhances diagnostic precision. Beyond diagnostic improvements, the abstract underscores the substantial influence of AI-driven methods on breast cancer treatment. The integration of AI not only increases diagnostic precision but also opens avenues for individualized treatment planning, marking a paradigm shift toward personalized medicine in breast cancer care. However, challenges persist, with issues related to data quality and interpretability requiring continued research efforts. Looking forward, the abstract envisions future directions for breast cancer identification and diagnosis, emphasizing the adoption of explainable AI techniques and global collaboration for data sharing. These initiatives promise to propel the field into a new era characterized by enhanced efficiency and precision in breast cancer care.