Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

A lump growing in the breast may be referred to as a breast mass related to the tumor. However, not all tumors are cancerous or malignant. Breast masses can cause discomfort and pain, depending on the size and texture of the breast. With an appropriate diagnosis, non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant. With the development of the artificial neural network, the deep discriminative model, such as a convolutional neural network, may evaluate the breast lesion to distinguish benign and malignant cancers from mammogram breast masses images. This work accomplished breast masses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets. A residual neural network 50 (ResNet50) model along with an adaptive gradient algorithm, adaptive moment estimation, and stochastic gradient descent optimizers, as well as data augmentations and fine-tuning methods, were implemented. In addition, a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models. The results of training accuracy, p-value, test accuracy, area under the curve, sensitivity, precision, F1-score, specificity, and kappa for adaptive gradient algorithm 25%, 75%, 100%, and stochastic gradient descent 100% fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.