Abstract: Skin diseases are among the most common medical conditions worldwide, affecting millions of people each year. Accurate and timely diagnosis is critical, yet traditional diagnostic methods depend heavily on dermatologists’ expertise and manual examination, which can lead to human error and delayed treatment. This paper presents an automated skin disease detection system using Convolutional Neural Networks (CNNs), a deep learning technique capable of learning complex visual features from medical images. The proposed model classifies skin lesions into different disease categories, such as melanoma, eczema, and psoriasis, using publicly available datasets like HAM10000. The CNN model is trained and validated on dermoscopic images, achieving high accuracy in disease identification.
Keywords: Skin Disease Detection, Deep Learning, Convolutional Neural Network, Image Classification, Medical Diagnosis.
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DOI:
10.17148/IJARCCE.2025.141199
[1] Ravindra Prasad, Megha K, Poorvika K J, Sandhya J V, Yashaswini C K, "Skin Disease Detection Using CNN," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141199
