INTELLIGENT BREAST CANCER IDENTIFICATION SYSTEM USING DEEP TRANSFER LEARNERS WITH SVM CLASSIFIER MODELS
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Breast cancer (BC) is the most common disease in the medical field. It is the second leading cause of cancer-related deaths after lung cancer, especially in women. Early detection and treatment of the disease are essential for the survival of patients. Ultrasound image analysis is one of the procedures for breast cancer identification. We collected ultrasound cancer image data from the Kagle Data Repository. Nowadays, automated intelligent cancer detection through image datasets using deep learning methods is one of the most efficient techniques. This research analyses and extracts the features of breast ultrasound cancer images using deep transfer learning neural networks such as Inception V3, VGG-16, and VGG-19 networks. The experimental transfer learners produce the image features (Inception-v3, VGG-16 net, and VGG-19 net). After that, we use the 10-cross folders SVM classifier with different kernels, such as the radial bias (RBF) function, polynomial, sigmoid, and linear, to classify into three categories that are benign, malignant, and normal. According to our observations, the Inception-v3 transfer learner produces quality features for classification. Its classification accuracy is higher than that of other transfer learners classified by the SVM with all kernels. Interestingly, the SVM polynomial kernel performs better for all features of transfer learning than the remaining SVM kernels. According to our findings, the Inception-v3+SVM (polynomial) performance is very high, with a 0.944 AUC and 87.44% classification accuracy, higher than other experimental models.
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