Hybrid Feature Selection Using the Firefly Algorithm for Automatic Detection of Benign/Malignant Breast Cancer in Ultrasound Images

The incidence rate of breast cancer (BC) is progressively increasing worldwide, and early diagnosis can help reduce the mortality rate. Ultrasound imaging, a cost-effective imaging technique, is widely used for initial screening of patients suspected of having breast cancer. Categorizing breast ultrasound images into benign and malignant classes is crucial for planning appropriate treatment strategies to combat BC. This research proposes a Convolutional Neural Network (CNN) framework to classify breast ultrasound images. This framework comprises the following stages: (i) image collection and resizing, (ii) CNN segmentation to extract the cancerous region, (iii) deep feature mining, (iv) extraction of handcrafted features, (v) selection of optimal features based on the Firefly algorithm (FA) and serial concatenation of features to create the feature vector, and (vi) performance evaluation and validation. The proposed classification task is executed using (i) deep-feature-based classification and (ii) integrated deep and handcrafted (hybrid) features. Experimental outcomes confirm that the ResNet18-based deep features achieve a classification accuracy of 91% with the SoftMax classifier, while the proposed hybrid features provide a classification accuracy of 99.50% with the K-Nearest Neighbor (KNN). These results underscore the significance of the proposed scheme.