Skin disease classification is a critical aspect of dermatological diagnostics, with millions of people globally affected by diverse conditions. Skin diseases, ranging from infectious to neoplastic, are traditionally classified manually, a process prone to time-consuming errors. Deep learning, notably Convolutional Neural Networks (CNNs), offers an effective solution by automatically learning hierarchical features from image data. The chosen EfficientNet architecture strikes a balance between model size and performance, making it ideal for resource-constrained environments such as healthcare settings. Manual classification of skin diseases, a traditionally time-consuming process prone to errors, necessitates a more efficient solution. Leveraging Convolutional Neural Networks (CNNs), notably the EfficientNetB4 architecture, We got a phenomenal training accuracy of $\mathbf{9 9 \%}$ and a robust testing accuracy of $\mathbf{9 6 \%}$. This signifies the efficacy of deep learning in automating the identification and classification of diverse skin conditions.


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    Title :

    Revolutionizing Skin Disease Diagnosis: Advanced Image Analysis and Precision Healthcare using EfficientNet




    Publication date :

    2024-11-06


    Size :

    1234579 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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