What question did this study set out to answer?

The aim is to develop an AI system for automated classification of skin diseases using advanced machine learning techniques.

March 1, 2026Open Access

Ai Based Skin Disease Diagnostic System

Key Points

The aim is to develop an AI system for automated classification of skin diseases using advanced machine learning techniques.
Developed an AI-based diagnostic system using deep learning and transfer learning techniques.
Employed the MobileNetV2 architecture for efficient image classification.
Trained and evaluated the model with the HAM10000 dataset of dermatoscopic images.
Applied preprocessing and data augmentation to enhance model performance.
Implemented the solution using TensorFlow and created a web interface with Streamlit.
Achieved an overall classification accuracy of 78%.
Demonstrated potential as a clinical decision-support tool for dermatology.
Emphasized the importance of ethical considerations in AI diagnostics.

Abstract

Skin diseases represent a significant global health concern due to their increasing prevalence and the limited availability of dermatology specialists, particularly in resource- constrained regions. Early and accurate diagnosis plays a crucial role in improving treatment outcomes and reducing disease progression. This paper presents an AI-Based Skin Disease Diagnostic System that leverages deep learning and transfer learning techniques for automated skin disease classification. The proposed system employs the MobileNetV2 architecture, chosen for its computational efficiency and strong performance on image classification tasks, making it suitable for real-world deployment. The model is trained and evaluated using the HAM10000 dataset, consisting of dermatoscopic images spanning seven different skin disease classes. Image preprocessing and data augmentation techniques are applied to improve generalization and robustness. The system achieves an overall classification accuracy of 78%, demonstrating its potential as a clinical decision-support tool. The proposed solution is implemented using TensorFlow and deployed via a Streamlit-based web interface for interactive usage. Ethical considerations are emphasized, and the system is explicitly designed as a supportive diagnostic aid, not a replacement for professional dermatologists. \\

Read Full Paperexternally

Bookmark

View Full Paper

Cite This Study

Lp et al. (Wed,) studied this question.

synapsesocial.com/papers/69a3d800ec16d51705d2e85e https://doi.org/https://doi.org/10.5281/zenodo.18796977

Bookmark

View Full Paper