Pinpoint the Aliment of Skin Disease With AI

Abstract

Dermatology is the field of science responsible for diagnosis and management of skin disorders. The dermatological disorders are wide spread and vary geographically, and usually due to the change in temperature, humidity and other environmental factors. Human skin presents one of the most complex systems to mechanically image and analyze due to its unevenness, color, presence of hair and other soothing features. Although, numerous researches are being done to find out and prove human skin As exploited (Computer Vision techniques), very few concentrated around the medical paradigm of the issue. Owing to lack of medical facilities available in the remote regions, patients often ignore early warning signs which could later worsen the situation over time. Thus, there is always a growing demand for automatic skin disease detection system with high accuracy of diagnosis. Therefore, we propose a system for automatic detection and classification of skin diseases for building a multi-class deep learning model to tell the difference between Healthy Skin and skin suffering from a disease and categorization of skin diseases into the following groups: Melanocytic Nevi, Melanoma, Keratosis like lesions, Basal cell Carcinoma, Actinic Keratoses, Vascular tumor, and Dermatofibroma. We have used Profound Learning to prepare our show. Profound Learning could be a subset of Machine Learning which uses a much bigger dataset than the traditional approach, thus the number of classifiers is reduced significantly. The machine is capable of learning all by itself, it sorts the provided data into different levels of prediction and very quickly, gives the precise results, thus helping and fostering the development of Dermatology. The algorithm that we have used is Convolutional Neural Arrange (CNN) because it is one of the foremost preferred calculation for picture classification

Introduction

Manufactured Insights is developing an AI-based automated system to predict and classify skin diseases using deep learning, particularly Convolutional Neural Networks (CNNs). Skin diseases, especially malignant melanoma, pose serious health risks due to their infectious nature and late detection. The project aims to create a non-invasive, accurate, and fast diagnostic tool by analyzing dermatological images from large, annotated datasets such as ISIC and HAM10000.

The methodology includes extensive data collection, cleaning, preprocessing (noise reduction, segmentation), feature extraction, and training optimized CNN models with techniques like dropout and L2 regularization to prevent overfitting. Model performance is evaluated using metrics such as accuracy, recall, precision, F1-score, and k-fold cross-validation. The system aims to be scalable and adaptable with future enhancements including multimodal learning, real-time monitoring, and integration with cloud and edge computing.

Literature highlights the evolution of AI in skin disease diagnosis, starting from traditional machine learning methods (SVM, Random Forest, KNN) to advanced deep learning architectures. Image preprocessing and segmentation play key roles in improving model accuracy. Large, publicly available datasets facilitate model training and validation. Future trends focus on improving model generalization, reducing bias, increasing interpretability, and enabling real-time monitoring via mobile apps and wearables.

Ultimately, the AI-driven approach promises to improve early detection, reduce healthcare costs, expand access to dermatological care, especially in underserved areas, and support personalized treatment plans.

Conclusion

AI has demonstrated to be a transformative device within the location and conclusion of skin illnesses, with critical en-hancements in demonstrate exactness and openness. Whereas profound learning, especially CNNs, has revolutionized the field, challenges such as inclination, information protection, explainability, and administrative obstacles stay. Progressing investigate into multimodal learning, reasonable AI, and more different datasets will be key to overcoming these deterrents and guaranteeing that AI can dependably serve as an even-handed and viable device in worldwide dermatological care.

References

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Copyright

Copyright © 2025 Anurag Rahangdale, Palash Bopate, Mohit Turkar, Rutika Chakole, Tanushree Patil, Prof. Priyanka Gupta . 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.