Machine Learning for Early Cancer Detection and Classification: AI-Based Medical Imaging Analysis in Healthcare

Abstract

A common kind of cancer is breast cancer. Raising the survival rate of breast cancer patients is mainly dependent onbreast cancer recurrence prognosis. The accuracy of cancer detection and diagnosis has increased with the progressof technology and ML approaches. Machine learning (ML) provides a number of statistical and probabilisticapproaches. This study introduces a deep learning-based approach to automatically classify breast cancer imagesfrom the BreakHis dataset. Feature extraction was performed using a Convolutional Neural Network (CNN) toautomatically detect significant tissue structures. The MobileNetV2 architecture was employed for its efficiency inhandling large-scale data while maintaining high classification accuracy. The model achieved an impressive accuracyof 98.18%, with precision of 98.38%, sensitivity of 97.37%, and an F1score of 97.85%. When compared to otherarchitectures, MobileNetV2 outperformed Xception, ResNet101, and EfficientNet, which demonstrated lower accuracyand sensitivity. These results highlight the potential of MobileNetV2 for reliable, fast, and cost-effective breast cancerdetection, offering a promising tool for clinical applications.