Skin Sentinel: An artificial intelligence-based skin cancer detection system

Abstract

Skin cancer is the most common malignancy worldwide, and effective early detection remains essential despite advances in prevention and photoprotection strategies. This study introduces an intelligent system for skin cancer diagnosis via deep learning methodologies. The suggested hybrid model combines the best aspects of MobileNet and long short-term memory (LSTM) networks to improve skin lesion image classification. The technology was designed not only to help doctors make accurate diagnoses, but also to support real-world healthcare tasks, including uploading images, automatically analyzing them, suggesting treatments, and scheduling appointments for patients and doctors. The experimental findings indicate that the proposed hybrid model achieves 93% accuracy, surpassing several current models, including support vector machine, convolutional neural network, Visual Geometry Group, ResNet, and MobileNet. Combining MobileNet with LSTM improves the ability to extract and classify features. Early detection of skin cancer can make therapy more effective and lower death rates. The suggested method shows great promise for use in real-world medicine. For future work, the system can be improved by using increasingly diverse datasets, more advanced deep learning architectures, and real-time clinical deployment to make diagnoses more accurate and reliable. The suggested system is intended to facilitate early skin lesion analysis and help medical practitioners with initial diagnosis by acting as an artificial intelligence-assisted screening and decision-support tool.

Keywords

Dermatological manifestation / Supervised machine learning / Convolutional neural network / MobileNet / ResNet / Hybrid algorithm

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