Skin Disease Detector using CNN

Anand Ranjan; Abhishrut Dutta; Jahid Hussain; Aryaan Ved; Vikram Kumar

doi:10.71097/IJSAT.v16.i2.3790

Skin Disease Detector using CNN

Author(s)	Anand Ranjan, Abhishrut Dutta, Jahid Hussain, Aryaan Ved, Vikram Kumar
Country	India
Abstract	Skin conditions are common and frequently need for a quick and precise diagnosis in order to be treated. In this paper, we propose a Convolutional Neural Network (CNN) based skin disease detection system. CNNs are ideally suited for the identification of skin diseases from photos because they have shown impressive effectiveness in image classification tasks. We make use of a sizable collection of skin picture annotations that span a wide variety of dermatological disorders. Multiple convolutional and pooling layers are used in our CNN architecture to automatically extract discriminative features from input photos. Using a variety of supervised learning strategies, we train the CNN model to maximize performance measures including accuracy, precision, recall, and F1-score. By means of comprehensive testing and analysis, we exhibit the efficacy of our CNN-based skin disease detector in precisely recognizing diverse skin
Keywords	Skin disease detection, Convolutional Neural Networks (CNN), dermatological disorders, image classification, supervised learning, accuracy, precision, recall, F1-score
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-22
Cite This	Skin Disease Detector using CNN - Anand Ranjan, Abhishrut Dutta, Jahid Hussain, Aryaan Ved, Vikram Kumar - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3790
DOI	https://doi.org/10.71097/IJSAT.v16.i2.3790
Short DOI	https://doi.org/g9gdtm

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Skin Disease Detector using CNN

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