Enhancing Dermatological Diagnosis with Optimized Deep Learning Lenet Model

Skin diseases pose a significant global health concern, demanding precise and prompt diagnosis. Current manual methods are labor-intensive, relying heavily on expert input and extensive manual labor. Our goal is to leverage deep learning techniques to develop an efficient, automated diagnostic tool for skin diseases. Challenges in existing approaches include their time-consuming nature and reliance on expert consultation. Technological solutions involve integrating computer vision, deep learning, and image processing techniques. Lenet and modified Lenet models are specifically used by the proposed architecture as Convolutional Neural Networks (CNNs). The advantages of employing deep learning include swift and automated disease detection, reduced manual effort, and economic benefits. A thorough assessment of contemporary research in deep learning-based skin disease diagnosis is integral to our research. Results demonstrate that deep learning methods can offer accurate and rapid disease detection, mitigating the shortcomings of manual methods. The significance of transitioning from manual to automated diagnostic tools is underscored in the conclusion, acknowledging persisting challenges. Future endeavors entail addressing obstacles in deep learning, exploring research avenues, and considering integration with emerging technologies for enhanced diagnostics.