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Dermatological disease prediction and diagnosis system using deep learning

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Abstract

The prevalence of skin illnesses is higher than that of other diseases. Fungal infection, bacteria, allergies, viruses, genetic factors, and environmental factors are among important causative factors that have continuously escalated the degree and incidence of skin diseases. Medical technology based on lasers and photonics has made it possible to identify skin illnesses considerably more rapidly and correctly. However, the cost of such a diagnosis is currently limited and prohibitively high and restricted to developed areas. The present paper develops a holistic, critical, and important skin disease prediction system that utilizes machine learning and deep learning algorithms to accurately identify up to 20 different skin diseases with a high F1 score and efficiency. Deep learning algorithms like Xception, Inception-v3, Resnet50, DenseNet121, and Inception-ResNet-v2 were employed to accurately classify diseases based on the images. The training and testing have been performed on an enlarged dataset, and classification was performed for 20 diseases. The algorithm developed was free from any inherent bias and treated all classes equally. The present model, which was trained using the Xception algorithm, is highly efficient and accurate for 20 different skin conditions, with a dataset of over 10,000 photos. The developed system was able to classify 20 different dermatological diseases with high accuracy and precision.

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Authors and Affiliations

  1. Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India

    Neda Fatima

  2. Jamia Millia Islamia, New Delhi, India

    Syed Afzal Murtaza Rizvi

  3. Army Hospital Research and Referral, New Delhi, India

    Major Syed Bilal Abbas Rizvi

Authors
  1. Neda Fatima
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  2. Syed Afzal Murtaza Rizvi
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  3. Major Syed Bilal Abbas Rizvi
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Correspondence to Neda Fatima.

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Impact statement

Dermatological illnesses have grown fairly common, and the expense of identifying them using sophisticated procedures is exorbitant. The current research proposes a low-cost and comprehensive solution based on deep learning algorithms that can distinguish between 20 distinct types of dermatological skin problems with high precision and accuracy over a large dataset of more than 10,000 photos.

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Fatima, N., Rizvi, S.A.M. & Rizvi, M.S.B.A. Dermatological disease prediction and diagnosis system using deep learning. Ir J Med Sci (2023). https://doi.org/10.1007/s11845-023-03578-1

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  • DOI: https://doi.org/10.1007/s11845-023-03578-1

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