Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

Abstract

A self-learning disease detection model will be useful for identifying skin infections in suspected individuals using skin images of infected patients. To detect skin diseases, some AI-based bioinspired models employ skin images. Skin infection is a common problem that is currently faced due to various reasons, such as food, water, environmental factors, and many others. Skin infections such as psoriasis, skin cancer, monkeypox, and tomato flu, among others, have a lower death rate but a significant impact on quality of life. Neural Networks (NNs) and Swarm intelligence (SI) based approaches are employed for skin disease diagnosis and classification through image processing. In this paper, the convolutional neural networks-based Cuckoo search algorithm (CNN-CS) is trained using the well-known multi-objective optimization technique cuckoo search. The performance of the suggested CNN-CS model is evaluated by comparing it with three commonly used metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO. This comparison was based on various measures, including accuracy, precision, recall, and F1-score. These measures are calculated using the confusion matrices from the testing phase. The results of the experiments revealed that the proposed model has outperformed the others, achieving an accuracy of 97.72%.

Convolutional Neural Network-Based Approach For Skin Lesion Classification

“…Melanoma is a lethal form of skin cancer responsible for claiming thousands of lives worldwide. (6) In recent years, substantial efforts have been dedicated to saving lives by enabling early detection. Various machinelearning approaches have been put forth for the segmentation and classification of skin lesions, aiming to enhance the chances of identifying melanoma at its earliest and most treatable stages.…”