Skin Disease Detection and Classification Using CNN

G Ranganadha Rao; G Likhitha .; M Bala Vardhini; S Poorna Chandra Rao; P Venkata Balaji; Sk Imran

doi:10.36948/ijfmr.2026.v08i02.72767

Skin Disease Detection and Classification Using CNN

Author(s)	Prof. G Ranganadha Rao, Ms. G Likhitha ., Ms. M Bala Vardhini, Mr. S Poorna Chandra Rao, Mr. P Venkata Balaji, Mr. Sk Imran
Country	India
Abstract	Skin diseases are among the most common health problems worldwide, affecting millions of people and requiring timely diagnosis for effective treatment. Early detection is especially critical for severe conditions such as melanoma, where delayed diagnosis can lead to life-threatening consequences. However, traditional diagnostic methods rely heavily on dermatologists’ expertise, which can be subjective, time-consuming, and limited in availability, particularly in rural and underserved regions. This paper presents an automated system for skin disease detection and classification using Convolutional Neural Networks (CNN), a powerful deep learning technique widely used in image analysis. The proposed model is designed to automatically extract important features such as color, texture, and lesion patterns from dermatoscopic images without the need for manual feature engineering. To enhance model performance, preprocessing techniques including image resizing and normalization are applied, along with data augmentation methods such as rotation, flipping, and zooming to improve generalization and reduce overfitting. The system is trained and evaluated on the HAM10000 dataset, which contains more than 10,000 labeled images across multiple categories of skin diseases. The CNN architecture consists of convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification, followed by a softmax function for output prediction. Experimental results demonstrate that the proposed model achieves high accuracy and performs effectively in distinguishing between different skin conditions. The proposed system can serve as a supportive diagnostic tool for dermatologists, enabling early detection, reducing diagnostic errors, and improving healthcare accessibility. Additionally, its computational efficiency makes it suitable for real-time applications, including mobile and web-based systems, thereby contributing to advancements in intelligent healthcare solutions.
Keywords	Skin Disease Detection, Convolutional Neural Networks (CNN), Deep Learning, Medical Image Analysis, Image Classification, HAM10000 Dataset, Artificial Intelligence, Feature Extraction
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-29
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.72767

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Skin Disease Detection and Classification Using CNN

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