A comparative study of deep learning algorithms for image-based classification of hyperpigmented skin disease.

There are growing numbers of significant skin disorders, including skin pigmentation. It states that skin color is determined by the amount of melanin produced by the body. The two main categories of skin pigmentation are hyperpigmentation, in which pigment seems to overflow, and hypopigmentation, in which pigment appears to decrease. However, many skin conditions share characteristics, making it difficult for dermatologists to correctly early diagnose their patients. Consequently, the accurate detection of skin disorders and the diagnosis of dermatoscopy pictures can be greatly aided by machine learning and deep learning approaches. The most effective deep learning technique for picture identification was investigated to diagnose hyperpigmented skin diseases. YOLO, DenseNet201, GoogLeNet, InceptionResNetV2, and MobileNet were among the pretrained models used to classify four most common hyperpigmented skin disorders. Using assessment metrics like accuracy and AUC (Area Under Curve), it was determined which deep learning method would work best for creating a clinical diagnostic system. The study analyzed the accuracy rates of five pretrained models, including GoogleNet, MobileNet, DenseNet201, InceptionResNetV2, and YOLO, after 50 iterations. Using metrics such as accuracy and Area Under the Curve (AUC), the study evaluated the models' performance on a small dataset split into 80% for training and 20% for testing. Training accuracy rates were 93.8%, 100%, 100%, 98.77%, and 97.43%, respectively, while test accuracy rates were 87.18%, 79.49%, 87.18%, 89.74%, and 97.56%. DenseNet201 showed strong performance, particularly for cafe-au-lait spots (CS), melasma (ML), and nevi (MN), but struggled with congenital nevus (CN). Despite DenseNet201′s strong traditional CNN capabilities and generalization, YOLO emerged as the top model due to its stable accuracy and AUC values, as confirmed by confusion matrices. While these models show promise as diagnostic tools for dermatologists, further research, including expanding the dataset and exploring hybrid models, is needed to enhance their clinical accuracy and effectiveness. [ABSTRACT FROM AUTHOR]