Enhancing Breast Cancer Diagnosis Using CNN and Transfer Learning Model

Abstract

Breast cancer diagnosis using histopathology images and advanced deep learning techniques. The study evaluates five models: MobileNetV2, ResNet50V2, DenseNet121, DenseNet201, and a custom CNN. Among these, DenseNet201 emerged as the most effective model, achieving a validation accuracy of 91.48% and a validation loss of 0.2134, showcasing its robust performance and superior generalization capabilities. Similarly, DenseNet121 demonstrated strong results with a validation accuracy of 88.92% and a validation loss of 0.3013, making it another reliable option for classification tasks.While ResNet50V2 exhibited the highest training accuracy of 97.64%, its validation accuracy of 82.66% highlighted the need for further fine-tuning to address potential overfitting. MobileNetV2 achieved a validation accuracy of 80.42%, emphasizing its efficiency in training with a low training loss of 0.1238 but limited generalization compared to the DenseNet models. The custom CNN achieved a validation accuracy of 87.26%, proving its capability as a lightweight alternative suitable for deployment in resource-constrained environments.The study also includes ethical considerations such as ensuring patient data privacy and equitable access to AI-driven diagnostics. Furthermore, a sustainability plan was devised to minimize environmental impact through energy-efficient practices and telemedicine applications. A user-friendly web application was developed to enable healthcare providers to access the trained models, facilitating their integration into clinical workflows. This research significantly advances breast cancer diagnostics, offering scalable, ethical, and accessible AI solutions to improve patient care and healthcare efficiency.