DeepSkinNet: ReLU-Enhanced CNNs for AI-based Skin Disease Detection
Rajkumar Pandiarajan, V. Hariharan, Mr. C. Abinesh, N Harrish
Abstract
A unique use of convolutional neural networks (CNNs) to improve the resilience and accuracy of skin disease identification is called DeepSkinNet. Skin disorders are illnesses that affect the skin. Rashes, inflammation, itching, and other skin abnormalities can be brought on by these illnesses. While lifestyle choices may be the cause of some skin disorders, genetics may also play a role. This research investigates the transformative potential of Convolutional Neural Networks (CNNs) in dermatology, specifically focusing on skin disease identification. A particular focus on skin disease diagnosis, the revolutionary potential of convolutional neural networks, or CNNs, in dermatology is examined in this paper. The study emphasizes how important early detection is, especially in light of the noticeable rise in melanoma occurrences. Using cutting-edge CNN-based methodologies and the Kaggle dataset and Web Scraping. A systematic preprocessing, including pixel normalization, rotation, flipping, standardization, and augmentation, enhances the dataset and neural network robustness. Convolutional layers for feature extraction and fully connected layers for classification make up the CNN architecture, which was influenced by ReLU and SoftMax. Batch normalization and dropout layers are added to the design to enhance generalization and decrease overfitting. The model undergoes iterative training with optimized learning rates and continuous hyperparameter adjustments. The methodology includes data collection, model training, and extensive data preprocessing. Additionally, it highlights recent advancements in dermatology, shedding light on innovative therapies and technological interventions. Integration into clinical workflows, with user-friendly interfaces, enhances diagnostic efficiency and accuracy in real-world practice. The aim is to enhance understanding, prompt early detection, and contribute to ongoing efforts in advancing dermatological research and patient care.