Skin Lesion Classification towards Melanoma Diagnosis using Convolutional Neural Network and Image Enhancement Methods

Abstract

Melanoma is the deadliest form of skin lesion which is a severe disease globally. Early detection of melanoma using medical images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging task. Since the joint use of image enhancement techniques and deep convolutional neural network (DCNN) has demonstrated proven success, the joint techniques could have discriminatory power on skin lesion diagnosis as well. To this hypothesis, we propose the aggregation algorithm for skin lesion diagnosis that utilize a DCNN to extract the local features classify medical images for melanoma disease. All experiments are performed using the data provided in International Skin Imaging Collaboration (ISIC) 2018 Skin Lesion Analysis towards Melanoma Detection. Experimental results show that our algorithm achieves excellent classification results for melanoma diagnosis