부산대학교 도서관

Breast cancer is a common gynaecological ailment that affects women all over the world. Early identification of this disease has been shown to be extremely beneficial in terms of therapy. Mammographic pictures are analysed in this article utilising image processing methods and a pipeline structure to see whether they contain malignant tumours, which are subsequently categorised. The SVM classifier is used for classification, and it is fed by the characteristics that have been picked. It is supported by a number of kernel functions. This differs from standard machine learning classification and optimisation strategies, and it is shown in a unique manner. The outcomes of the actualised computer-aided diagnostic (CAD) learning process are analysed in order to determine whether or not it was successful. The BCDR-F03 dataset is evaluated, as well as the: 1) local mammographic dataset; 2) colony optimisation-based multi-layer perceptron (ACO-MLP) dataset.