EfficientSkinDis: An EfficientNet-based classification model for a large manually curated dataset of 31 skin diseases

Skin diseases are a common health issue, affecting nearly one-third of the global population, but they are often underestimated in terms of their impact despite being highly visible. Accurately diagnosing skin diseases can be challenging sometimes. Fortunately, deep learning algorithms have shown great potential for a variety of tasks, including the diagnosis of skin diseases. This research presents a novel dataset of 31 skin diseases by blending two different datasets. Three different CNN types, which were EfficientNet, ResNet, and VGG with different architectures, were used for transfer learning on the skin disease dataset. EfficientNet had the highest testing accuracy, thus, it was further trained for fine-tuning. Initially, using the training split of 70%, the EfficientNet model achieved a testing accuracy of 71%. Therefore, as this was considered low, the 70% training split comprising 3,424 samples was further augmented. As a result, the model's accuracy improved to 72%. However, it was observed that the data split of 70/30 was not effective, thus the experiment was re-conducted with a train-test split of 80/20 and an improvement in accuracy score i.e., 74% was observed. To further improve the model's accuracy, augmentations were applied to the data and were able to achieve an accuracy of 87.15%. Finally, the trained model with the best accuracy was deployed on a Streamlit webserver. The proposed model can help society in diagnosing skin disease early, therefore it can be treated timely. The webserver of EfficientSkinDis can be accessed at (https://abdulrafay97-efficientskindis-app-ooncon.streamlit.app/).