Abdul Rafay2026-02-152026-02-152022https://escholar.umt.edu.pk/handle/123456789/19779Skin conditions are widespread, impacting almost a third of people worldwide, yet their significance is often overlooked despite being easily visible. In reality, skin diseases are the fourth most common cause of all human illnesses. They are a significant global health concern, but diagnosing them can be difficult at times. Luckily, deep learning techniques have demonstrated promise in various areas, including identifying skin diseases. In this study, we blended two datasets to generate a new dataset of 31 skin diseases and used it to train the model. Three different architectures were tested for transfer learning on the skin disease dataset, which were EfficientNet, ResNet, and VGG. EfficientNet had the highest testing accuracy, and it was then fine-tuned further. The EfficientNet model was initially trained using 70% of the data, and it achieved a testing accuracy of 71%. However, it was later determined that the 70/30 data split was not effective, so the experiment was repeated with a 80/20 train-test split, resulting in an improved accuracy of 74%. Additional data augmentation was applied to improve the model's accuracy even more, resulting in a final accuracy of 86%. The model with the highest accuracy was then deployed as an API on a server and made available to the public to aid in early diagnosis and timely treatment of skin diseases.enThesis