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Automated Detection and Classification of Skin Diseases Using Diverse Features and Improved Gray Wolf‐based Multiple‐layer Perceptron Neural Network by K. Melbin; Y. Jacob Vetha Raj is a Computer Science article available to read on EtoBox.

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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 mu

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Author

K. Melbin; Y. Jacob Vetha Raj

Publisher

Wiley

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)