Vision beyond the human eye: advancing early breast cancer detection through computer vision and ai-enhanced imaging
Godfrey Perfectson Oise, Babalola Eyitemi Akilo, Joy Akpowehbve Odimayomi, Unuigbokhai Nkem Belinda, Chioma Julia Onwuzo, Onoriode Michael Atake, Sofiat Kehinde Bakare · Advances in Computing and Engineering · 2025
This paper reviews the application of computer vision and artificial intelligence (AI) in enhancing breast cancer detection, exploring how deep learning models, particularly Convolutional Neural Networks (CNNs), augment traditional screening techniques. The review examines the current state of computer vision applications in breast cancer detection, emphasizing deep learning-based approaches, and discusses how CNNs are integrated into clinical workflows, the empirical evidence supporting their effectiveness, and the practical challenges involved in their clinical adoption. The methodology also includes a deep learning-based approach to classify and segment breast ultrasound images using a publicly available dataset. CNN-based systems demonstrate performance on par with or even surpassing human radiologists in specific diagnostic tasks. Studies show that MobileNetV3, a lightweight CNN, holds strong potential for integration into edge AI systems for point-of-care diagnostics, as well as in privacy-preserving frameworks such as federated learning. The MobileNetV3-based classification model demonstrated robust performance across the three diagnostic categories: normal, benign, and malignant, with an overall test set accuracy of 91.2%. Key performance metrics, including precision (benign: 0.85, malignant: 0.74, normal: 0.83), recall (benign: 0.84, malignant: 0.74, normal: 0.88), F1-score (benign: 0.85, malignant: 0.74, normal: 0.86), and accuracy (0.82), are examined to evaluate the efficacy of these AI-driven approaches. The review identifies emerging trends, such as multi-modal learning and federated learning, which aim to enhance model robustness and privacy. The integration of AI into clinical workflows holds promise for improving diagnostic accuracy and reducing healthcare disparities by expanding access to high-quality screening services. This paper contributes to a deeper understanding of how AI-driven innovations are reshaping breast cancer detection and inspires further research toward their responsible and widespread implementation. Received on, 11 May 2025 Accepted on, 18 June 2025 Published on, 02 October 2025