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The most lethal and devastating form of cancer, breast cancer, is often first detected when a lump appears in the breast. The cause can be attributed to a typical proliferation of cells in the mammary glands. Early breast cancer detection improves survival. Breast cancer screening and early detection are commonly carried out using imaging techniques such as mammography and ultrasound. Convolutional neural networks (CNNs) can identify breast cancer on mammograms. Layers of artificial neurons detect patterns and properties in images to help identify abnormalities more accurately. CNNs may be trained on large datasets to improve accuracy and handle more complex visual information than traditional methods. We introduced a unique approach termed BreastNet-SVM with the objective of automating the identification and categorization of breast cancer in mammograms. This study uses a nine-layer model with two fully connected layers to retrieve data features. Furthermore, we utilized support vector machines (SVM) for classification purposes. To conduct this experiment, we used a well-known benchmark dataset (DDSM). It is shown that the suggested model has a 99.16% accuracy rate, a 97.13% sensitivity rate, and a 99.30% specificity rate. The top approaches for detecting breast cancer were compared to the recommended BreastNet-SVM model. In terms of accuracy, the proposed BreastNet-SVM model fared better in experimental results on a DDSM dataset.

Breast Cancer Detection Using Deep Learning: An Investigation Using the DDSM Dataset and a Customized AlexNet and Support Vector Machine