Enhanced Skin Diseases Prediction using DenseNet-121: Leveraging Dataset Diversity for High Accuracy Classification

Skin disorders are a major worldwide health concern that require sophisticated diagnostic technologies for prompt and accurate identification. A deep learning approach that utilizes the DenseNet architecture for dermal image-based skin disease prediction is proposed. Creating and organizing a heterogeneous dataset covering a variety of skin conditions is part of the technique. Testing, verification, and training sets of the dataset are separated out to provide a thorough assessment of the model’s efficacy. Following its initial training on a large-scale dataset using pre-learned weights, the selected DenseNet model is refined using the skin disease dataset. Transferring expertise allows models to better identify relevant characteristics by utilizing information acquired from general photo recognition tasks. To adapt the architecture for the classification of skin diseases into many classes, custom layers are added. During the training phase, the model is optimized using suitable learning rates and loss functions. To reduce overfitting and improve the model’s generalization, hyperparameter adjustment is done. To assess the model’s performance, parameters like precision and loss are applied to the verification dataset. The model is evaluated on an alternative dataset to see if it can generalize to cases that haven’t been discovered yet once it has been successfully trained and validated. Throughout the process, patient privacy and data security are two of the most important ethical factors. The developed skin disease prediction model has the potential to be used in practical settings, facilitating prompt and precise diagnosis. Subsequent research endeavors could encompass ongoing enhancements to the model, cooperation with healthcare experts for clinical verification, and incorporation into user-friendly programs to ensure broad accessibility within healthcare environments.