Deep-learning feature extraction with their subsequent selection and support vector machine classification of the breast ultrasound images

Our study aimed to develop a comprehensive system for discriminating between benign and malignant breast lesions on ultrasound images. The system integrated deep learning (DL) and conventional machine learning techniques. Our database consisted of 494 ultrasound images, comprising 231 benign and 263 malignant breast lesions. In the initial stage, we evaluated the performance of non-modified DL networks, including VGG-16, ResNet-18, and InceptionRes-NetV2. We assessed the results for the entire lesion as well as its inner and outer parts. For training the networks, we employed supervised transfer learning. In the second stage, we utilized a support vector machine (SVM) model for lesion classification. The features obtained from the modified DL networks, where we removed the last layers, were used for training and testing the SVM. In the final stage, we assessed the classification results using SVM, with a focus on selecting the most significant features obtained from the modified DL networks. We employed techniques such as ReliefF, FSCNCA, and LASSO for feature selection. Our three-step approach yielded impressive results, with an accuracy of 0.987, sensitivity of 0.989, and specificity of 0.983. These results significantly outperformed using only DL or DL+SVM without feature selection. Overall, our algorithm demonstrated sufficient accuracy in the clinical task of discriminating between benign and malignant breast lesions on ultrasound images.