Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends
DOI:
https://doi.org/10.62486/latia2025117Keywords:
Breast cancer, AI-based algorithms, CNNAbstract
Numerous studies have highlighted the significance of artificial intelligence (AI) in breast cancer diagnosis. However, systematic reviews of AI applications in this field often lack cohesion, with each study adopting a unique approach. The aim of this study is to provide a detailed examination of AI's role in breast cancer diagnosis through citation analysis, helping to categorize the key areas that attract academic attention. It also includes a thematic analysis to identify the specific research topics within each category. A total of 30,200 studies related to breast cancer and AI, published between 2015 and 2024, were sourced from databases such as IEEE, Scopus, PubMed, Springer, and Google Scholar. After applying inclusion and exclusion criteria, 32 relevant studies were identified. Most of these studies utilized classification models for breast cancer prediction, with high accuracy being the most commonly reported performance metric. Convolutional Neural Networks (CNN) emerged as the preferred model in many studies. The findings indicate that both the quantity and quality of AI-based algorithms in breast cancer diagnosis are increases in the given years. AI is increasingly seen as a complement to healthcare sector and clinical expertise, with the target of enhancing the accessibility and affordability of quality healthcare worldwide.
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Copyright (c) 2025 Mutaz Abdel Wahed, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah (Author)
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