Gabor wavelet-based deep learning for skin lesion classification

Skin cancer cases are increasing and becoming one of the main problems worldwide. Skin cancer is known as a malignant type of skin lesion, and early detection and treatment are necessary. Malignant melanoma and seborrheic keratosis are known as common skin lesion types. A fast and accurate medical diagnosis of these lesions is crucial. In this study, a novel Gabor wavelet-based deep convolutional neural network is proposed for the detection of malignant melanoma and seborrheic keratosis. The proposed method is based on the decomposition of input images into seven directional sub-bands. Seven sub-band images and the input image are used as inputs to eight parallel CNNs to generate eight probabilistic predictions. Decision fusion based on the sum rule is utilized to classify the skin lesion. Gabor based approach provides directional decomposition where each sub-band gives isolated decisions that can be fused for improved overall performance. The results show that the proposed method outperforms alternative methods in the literature developed for skin cancer detection.