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The segmentation of dermoscopic‐based skin lesion images is considered to be challenging owing to various factors. Some of the most tangible reasons include poor contrast near the affected skin lesion, the fuzzy and unpredictable lesion limits, the presence of variations in noise, and capturing images under different conditions. This paper aims to develop an efficient segmentation model for dermoscopic images of different skin lesions based on deep learning. This paper proposes the 11‐layer convolutional deep neural network with two segmentation models trained from start to finish and do not depend on any previous information about the data. The viability, efficiency, and speculation ability of the models are evaluated on the ISIC2018 database. The proposed model achieves 0.903 accuracy and 0.820 Jaccard index in the segmentation of skin lesions. The model shows better performance compared to other image segmentation techniques from the leaderboards of ISIC2018 using deep learning. [ABSTRACT FROM AUTHOR]

Among various types of skin diseases, skin cancer is the deadliest form of the disease. This paper classifies seven types of skin diseases: Actinic keratosis and intraepithelial carcinoma, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanoma, Melanocytic type, and Vascular lesions. The primary objective of this paper is to evaluate the performance of these deep learning networks on skin lesion images. The lesion classification is implemented through transfer learning on fourteen deep learning networks: AlexNet, GoogleNet, ResNet50, VGG16, VGG19, ResNet101, InceptionV3, InceptionResNetV2, SqueezeNet, DenseNet201, ResNet18, MobileNetV2, ShuffleNet and NasNetMobile. The dataset used for these experiments are from ISIC 2018 of about 10,154 images. The results show that DenseNet201 performs best with 0.825 accuracy and improves skin lesion classification under multiple diseases. The proposed work shows the various parameters, including the accuracy of all fourteen deep learning networks, which helped build an efficient automated classification model for multiple skin lesions. [ABSTRACT FROM AUTHOR]