Deep Learning-Based Automated Skin Disease Detection and Classification from Dermoscopic Images

Abstract

Skin conditions cause health problems for a large number of people worldwide [11],[12]. However, a considerable percentage of people suffering from skin problems remain undetected due to limited access to dermatologists [12] and also because of the unreliable nature of online information. This paper introduces an intelligent web-based platform that can automatically detect and classify skin diseases through a deep learning approach. The system uses an EfficientNet-B3 model for precise categorization of images and is capable of producing results as high as 90.2% accuracy in recognizing different types of skin disorders [2],[5],[15]. In addition, the platform offers lesion segmentation through U-Net DeepLabV3 method [4], explainable AI visualization by Grad-CAM heatmaps [3] and a healthcare system that not only detects disease but also provides PDF reports helps users find location-based dermatologists and pairs them with AI powered chatbots for assistance. Developed using secure PHP, MySQL backend combined with responsive HTML, CSS and JavaScript frontend this platform makes it feasible for users to carry out preliminary dermoscopic evaluation of various skin lesions using a standard camera or smartphone in real time. In fact, the system solutions the issue of healthcare accessibility in several ways by offering instantaneous AI supported diagnosis by making the time gap for diagnosis lesser, and by making the dermatological screening process available to people who don\'t have direct contact with specialists.

Introduction

The text presents a deep learning-based system for early detection of skin diseases using dermoscopic images, addressing the limited access to dermatologists, especially in rural areas, and the risks of self-diagnosis. The proposed solution provides an AI-powered web platform where users can upload skin images and receive instant preliminary diagnoses along with confidence scores, downloadable reports, dermatologist suggestions, and a chatbot for guidance.

The system is built using EfficientNet-B3 for disease classification, U-Net for lesion segmentation, and Grad-CAM for explainable AI. Segmentation removes background noise and focuses on the lesion area, improving accuracy. EfficientNet-B3 extracts multi-level image features efficiently, enabling accurate classification of diseases such as melanoma and benign lesions while maintaining low computational cost. Grad-CAM improves transparency by generating heatmaps that show which regions influenced the model’s decision.

The literature review highlights that CNN-based models are widely used for skin disease detection and can reach dermatologist-level performance, but many existing systems lack efficiency, segmentation, or explainability. This motivates the integration of advanced architectures with a complete, user-friendly diagnostic system.

The methodology describes a three-stage pipeline: preprocessing and segmentation of images, classification using EfficientNet-B3, and explainability using Grad-CAM. Images are resized, normalized, and enhanced before processing. U-Net-based segmentation isolates lesions, and the classifier predicts disease categories.

Conclusion

This piece of research indicates how deep learning and intelligent healthcare platforms can enhance the availability of dermatological diagnosis. The system at hand employs recent deep learning methods including EfficientNet-B3 for the classification task U-Net based segmentation and Grad- CAM for explainable AI visualization. The developed model got 90.2% as the overall classification accuracy which means that AI can support skin disease detection at an early stage through dermoscopic images. The platform is a comprehensive solution that merges automatic disease identification, report generation, dermatologists\' search and patient support aspects. Such a system can be a big step forward in decreasing diagnosis time and preliminary skin disease screening can be made accessible to people living in remote and underserved areas where dermatologists are hardly available. Besides, a transparent and explainable AI based approach allows the establishment of trust between technology and healthcare professionals. The system can be enhanced in many ways in the future. For instance, more types of skin diseases can be added if the system has the capability to diagnose up to 7 types at present only. Besides, access of doctors to patient history can be made easy by integrating the system with hospital records. Moreover, patients and dermatologists can get in touch remotely through the use of telemedicine tools such as live chat. Another way to enhance the system would be the development of mobile applications thereby providing easier access to users. Incorporating multiple languages support will allow people from various ethnic backgrounds to be able to use the system. Lastly, the system needs to undergo testing with more diverse datasets before it can be utilized in the real world. Ongoing research and development can enable this system to become a dependable AI assistant for skin disease identification and also aid in making healthcare more accessible to the global community.

References

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Copyright

Copyright © 2026 Om Awate, Viraj Adake, Abhinandan Goswami, Avantika Chatterjee, Sarthak Bote, Mrs. Anuja Zade . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.