Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images

Abstract

Skin is one of the most significant organs, which serves as a barrier to the outside surroundings of the human body. To improve mortality, skin disease detection at a prior stage is necessary else it may convert to skin cancer. But its diagnosis at the prior stage that may increase life expectancy is a great experiment since it has a similar look to skin diseases. To deal with biomedical images, a new innovative automated system is required that can quickly and precisely identify skin lesions. Deep Learning is attracting a lot of attention in the treatment of numerous disorders. A fusion model is proposed here with the integration of the U-Net and Convolution Neural Network model. For this, the U-Net model has been utilized to segment the diseases using skin images and the Convolution Neural Network model has been proposed for the multi-class classification of segmented images. The model is simulated and analyzed using the HAM10000 dataset having 10,015 dermoscopy images of seven different classifications of skin diseases. The proposed model has been analyzed using two optimizers i.e. Adam and Adadelta on 20 epochs and 32 batch sizes for the skin disease classification. The model has outperformed on Adadelta optimizer with an accuracy value of 97.96%.