Diagnosis of skin diseases using Convolutional Neural Networks

Diagnosing skin diseases accurately can be challenging due to the complexity and variability of dermatological conditions. Traditional diagnosis often requires extensive testing and is heavily reliant on the experience of the practitioner, leading to potential delays and inconsistencies in treatment. To address these challenges, we propose a system that utilizes Convolutional Neural Networks (CNNs) for the automated diagnosis of skin diseases. The process begins with the input of a skin image, followed by image enhancement and segmentation to isolate relevant features. The CNN then classifies the skin condition as either benign or malignant. If a malignant condition is detected, the system further classifies the disease into early, intermediate, or advanced stages. This classification is accompanied by treatment options and lifestyle recommendations, all integrated into a user-friendly graphical interface (GUI). The proposed system aims to provide consistent and accurate diagnoses, reducing the dependency on practitioner experience and expediting the treatment process. The model is designed to achieve high accuracy, making it a reliable tool for early and effective intervention in dermatological care.

Keywords: Dermatology, Image Processing, Computer Vision, Deep Learning, Artificial Intelligence, Neural Network, Deep Learning, Computational Intelligence, Automated Disease Diagnosis, Convolutional Neural Network and GUI.