Benign and Malignant Breast Cancer Classification Using Deep Learning Algorithms and MIAS Mammography Dataset

Worldwide, breast cancer is quite widespread among many types of cancer. Early detection is crucial for effective treatment. While early detection does not cure cancer or prevent its recurrence, it significantly improves treatment outcomes. Regular breast cancer check-ups, including mammograms, play a vital role in early detection. The type of the observed tumour is also crucial. Therefore, our study utilized a range of deep learning methods to accurately classify distinct forms of breast cancer cells, including both benign and malignant varieties. The problem addressed in the study relies on the classification of tumour images as either benign or malignant. We utilized mammogram images from the MIAS dataset and implemented ten deep learning models: VGG16, VGG19, DenseNet121, DenseNet169, ResNet50, ResNet101, MobileNet, MobileNetV2, InceptionV3, and InceptionResNetV2. Our investigation demonstrated that some models had superior performance concerning classification accuracy and other performance parameters. Inceptionv3 is the deep learning model with the lowest accuracy value in the classification of breast cancer for MIAS mammogram data, while ResNet50 is the most accurate model. The top-performing model achieves an accuracy of 0.9691 using the ResNet50 architecture.