DermaAI: An AI-Powered Skin Disease Detection System Using Deep Learning

Abstract

Skin diseases represent a vast proportion of primary care visits, and their prompt and correct diagnosis may necessitate expertise on the side of a specialist. Artificial intelligence has potential to help screening skin diseases through analyzing skin lesion images. In this paper, the author will discuss the implementation of DermaAI, a multi-class skin disease detection system based on integrated deep-learning. We have prepared a source labeled dataset of dermatological pictures (cz/dermatitis, psoriasis, fungi, acne, rosacea, and vitiligo) and trained an EfficientNetB0 convolutional neural network (CNN) with transfer learning. This model was trained in two stages: one with training the newly-added layers only, and second fine-tuning the entire network, and the methods include data augmentation, class re-sampling, and learning rate adjustment using the form of a callback. It was implemented through an offline inference application using TensorFlow Lite and a Flask web interface and Android application. DermaAI has high accuracy (around 92 percent on a held-out test set) on classifying the target conditions. An error analysis (with confusion matrix) indicates that the error rate is strong on all classes with most confusion made among clinically similar categories. The system gives probabilities per class to help with the estimation of confidence. These findings indicate that DermaAI may be used as an effective dermatological tool, particularly in resource-constrained environments with a low number of specialists available[1], [2].

Introduction

Skin diseases affect a large portion of the population, and many people lack access to dermatologists, especially in rural or under-resourced areas. This often leads to misdiagnosis or delayed treatment. Advances in artificial intelligence (AI) and deep learning, particularly Convolutional Neural Networks (CNNs), have made it possible to analyze medical images and assist in diagnosing skin diseases.

To address limitations of existing AI systems that focus on only a few diseases, this study developed DermaAI, a deep-learning system designed to detect multiple skin diseases. The model is based on EfficientNetB0 CNN with transfer learning and is deployed as both a Flask web application and an Android mobile app, allowing online and offline usage. The system provides predicted disease labels along with probability scores indicating confidence levels.

DermaAI works through four main stages: image acquisition and preprocessing, optional lesion segmentation, CNN-based feature extraction and classification, and result presentation. The dataset used includes thousands of labeled images of common skin diseases such as eczema, psoriasis, fungal infections, acne, rosacea, and vitiligo. Data augmentation and class balancing techniques were applied to improve model performance.

The model was trained using transfer learning with the Adam optimizer and categorical cross-entropy loss, and evaluated using accuracy, precision, recall, and F1-score. Results showed that the system achieved about 94% validation accuracy and 92% test accuracy, with most disease classes achieving over 90% precision and recall. The system also demonstrated fast performance, taking 1–2 seconds on a web server and less than 1 second on a smartphone to produce predictions.

DermaAI provides a practical AI-based screening tool that can assist in early detection of skin diseases and improve accessibility to dermatological care. However, it has limitations such as dataset size, possible bias toward lighter skin tones, reliance only on images without patient metadata, and lack of external clinical validation.

Future improvements include expanding the dataset, adding severity detection, enabling real-time camera analysis, integrating tele-dermatology support, and providing multilingual interfaces. Overall, DermaAI demonstrates the potential of AI systems to support accessible and efficient skin disease diagnosis.

Conclusion

We have introduced the DermaAI, a complete deep learning framework of skin diseases identification, which includes a high-performance CNN (EfficientNetB0) along with easy-to-access deployment. With transfer learning and trained on a curated multi-class dataset, our model attained high accuracy (~92) in the classification of the common dermatoses. The implementation on the system is done in two programs (both web and mobile) hence wide applicability. We believe that, based on our assessment, the DermaAI will be able to deliver accurate and timely disease predictions and probability, which can be used to assist with early screening and triage. Although it is not intended to replace clinical judgment, this tool has shown how AI can contribute to the dermatological care, particularly in underserved areas. The architecture of DermaAI (the integration of the segmentation, the confidence scoring, and the delivery on cross-platform) covers most of the gaps evident in other solutions. Our work will in the future validate the system in the clinical setting and keep on the further improvements of its performance and scope. We hope that DermaAI can become a successful example of AI-based healthcare support, which will allow making dermatology available to anyone on the tip of the hat in the hallmark of the healthcare inquiry.

References

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Copyright

Copyright © 2026 Suyash Modi, Yogendra Kumar Maurya. 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.