One of the largest organs of the human body is the skin and its pigmentation differs among the population. During skin disease identification, the dermatologist requires a high level of expertise and accuracy. This study proposes different kinds of skin image feature extraction and classification methods. In this work, we have chosen six kinds of skin diseases such as melanoma, seborrheic keratosis, eczema, actinic keratoses, nevus, and lupus erythematosus. In the preprocessing stage, the color standardization is performed by gray world color constancy (GWCC) algorithm. The thick and thin hairs from the disease images are removed in the preprocessing stage. The circular kernel with morphological operation clearly segments the skin lesion region. Moreover, the combination of novel cumulative-based level difference mean (NCLDM) and improved Asymmetry, Border Irregularity, Color Variation and Diameter (ABCD) features vector (ABCD-fv) methods is more helpful to extract the shape, texture, and color feature of skin lesion. However, the more features never offer an accurate classification result, so we go for the ranking and selection of features. Finally, the improved gray wolf-based multiple-layer perceptron (IGWO-MLP) technique is used to produce the relevant skin disease class. For the experimentation, there are six datasets such as DermNet, Xiangya, Medicine Net, PH 2 , Kaggle, and HAM-10000, which are chosen for effective skin disease identification. The proposed method demonstrates better segmentation, feature extraction, and classification result in terms of accuracy, specificity, sensitivity, Jaccard similarity index, and Dice similarity index. As a result, the skin disease identification of the proposed IGWO-MLP method yields 98% accuracy, 99% sensitivity, 98% specificity, 98% Jaccard Similarity Index, and 99% Dice similarity index when compared with state-of-the-art methods. This work will certainly help dermatologists to make their work more efficient and help them to provide the correct treatment for the skin disease detected.
