phat 23(1):

Research Article

Skin Disease Classification Using CNN Algorithms

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  • @ARTICLE{10.4108/eetpht.9.4039,
        author={Raghav Agarwal and Deepthi Godavarthi},
        title={Skin Disease Classification Using CNN Algorithms},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        keywords={Skin disease classification, Transfer learning, Deep learning, Medical images, Clinical decision support system},
  • Raghav Agarwal
    Deepthi Godavarthi
    Year: 2023
    Skin Disease Classification Using CNN Algorithms
    DOI: 10.4108/eetpht.9.4039
Raghav Agarwal1, Deepthi Godavarthi1,*
  • 1: Vellore Institute of Technology University
*Contact email:


  INTRODUCTION: Dermatological disorders, particularly human skin diseases, have become more common in recent decades. Environmental factors, socioeconomic problems, a lack of a balanced diet, and other variables have all contributed to an increase in skin diseases in recent years. Skin diseases can cause psychological suffering in addition to physical injury, especially in people with scarred or disfigured faces. OBJECTIVES: The use of artificial intelligence or computer-based technologies in the detection of face skin disorders has advanced dramatically over time. Even for highly experienced doctors and dermatologists, identifying skin disorders can be tricky since many skin diseases have a visual affinity with the surrounding skin and lesions. METHODS: Today, the majority of skincare specialists rely on time-consuming, traditional methods to identify disorders. Even though several research have demonstrated promising results on the picture classification job, few studies compare well-known deep learning models with various metrics for categorizing human skin disorders. RESULTS: This study examines and contrasts various skin illnesses in terms of cosmetics and common skin concerns. Our dataset includes over 25000 of the eight most common skin disorders. Convolutional neural networks have shown imaging performance that is comparable to or greater than that of humans. We used 11 different network algorithms to identify the illnesses in the sample and compared the results. CONCLUSION: To adjust the format of incoming photographs, we do certain image pre-processing and image scaling for each model. ResNet152 beat other deep learning methods in terms of recall, accuracy, and precision on a test dataset of 1930 images.