Design of an AI Layer for Real-Time Skin Cancer Diagnosis.

Skin cancer is a common and potentially fatal disease that necessitates early and precise diagnosis for effective treatment. In recent years, artificial intelligence has shown promise in aiding dermatologists in the diagnosis of skin cancer. However, the inability of AI models to be interpreted hinders their adoption in clinical practice. This paper presents the design of an AI-based architecture for the real-time diagnosis of skin cancer in an effort to address such issues. The proposed system employs a collection of artificial intelligence (AI) models, including Decision Trees, Rule-Based Models, Logistic Regression, and Deep Forest Models, to achieve accurate and interpretable skin cancer diagnosis. Each model contributes its strengths to the ensemble, thereby enhancing the performance and interpretability of the whole. The ensemble method combines the benefits of various models to compensate for their shortcomings. The effectiveness of the proposed system is demonstrated by the analysis of a Skin Cancer MNIST, ISIC, and Mendeley Skin Cancer Datasets with nearly 250K samples, with 98.9% accuracy, 99.5% precision, and 98.5% recall. The system outperforms existing skin cancer diagnosis methods. The achieved accuracy and performance metrics indicate the system's potential as a reliable real-time diagnostic tool for dermatologists. The proposed system's use cases are diverse. Dermatologists can use the real-time skin cancer diagnosis system to accelerate the screening process, improve diagnostic accuracy, and improve patient outcomes in clinical settings. The models are selected for their ability to capture complex relationships in data, with each model contributing its individual strengths to the ensemble, thereby enhancing the performance and interpretability of the whole. In addition, the system can be integrated into telemedicine platforms, allowing remote patients to receive preliminary assessments and guidance from AI models prior to seeking additional medical care scenarios. [ABSTRACT FROM AUTHOR]