Aug 2026· International Journal of Technology and Emerging Research· 0 citations· 11 references
TL;DR
Experimental results demonstrate that the proposed Skin Disease Prediction System achieves high accuracy and reliability in predicting multiple skin diseases.
Abstract
The increasing prevalence of skin diseases and
the shortage of dermatologists in many regions
have created a need for automated and efficient
diagnostic systems. Traditional diagnosis relies
heavily on expert analysis, which may be time-
consuming and inaccessible in remote areas.
This paper proposes a Skin Disease Prediction
System that utilizes machine learning and deep
learning techniques to detect and classify skin
diseases from images. The system employs a
convolutional neural network (CNN) model
trained on a large dataset of skin disease
images. It integrates a full-stack architecture
consisting of a frontend interface for user
interaction, a backend server for processing,
and a database for storing patient records and
predictions. The system ensures accurate
classification, fast processing, and scalability.
Experimental results demonstrate that the
proposed system achieves high accuracy and
reliability in predicting multiple skin diseases.
This solution can assist dermatologists, improve
early diagnosis, and enhance healthcare
accessibility.
Keywords: deep learning; HAM10000; image processing; CNN; Skin Disease Detection; Artificial Intelligence; Healthcare Systems.
The proposed system uses Convolutional Neural Networks (CNNs) and related deep learning techniques to automatically learn discriminative visual features from skin-lesion images and classify disease categories to demonstrate the potential of artificial intelligence in dermatology.
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