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Enhanced Model for Classifying Skin Diseases Using YOLO Technique

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  • Mohamed Saied El-Sayed Amer

    (Canadian International College, New Cairo, Egypt)

Abstract

One of the most prevalent disorders is skin disease. Skin disorders are difficult to classify because of their complex classifications, early-stage symptoms that are quite similar, and highly imbalanced lesion samples. Simultaneously, given a small amount of data, a single trustworthy convolutional neural network model has poor generalization capacity, insufficient feature extraction capability, and low classification accuracy. Thus, based on model fusion, we suggested a classification model based on YOLO for the categorization of skin diseases in this research. A computer vision model in the You Only Look Once (YOLO) family is called YOLO. YOLO is frequently utilized for object detection. YOLO is available in four primary variants, with increasing accuracy rates: small (s), medium (m), large (l), and extra large (x). The training time for each version varies as well.

Suggested Citation

  • Mohamed Saied El-Sayed Amer, 2025. "Enhanced Model for Classifying Skin Diseases Using YOLO Technique," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(15), pages 645-654, April.
    • Handle: RePEc:bjc:journl:v:12:y:2025:i:15:p:645-654
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