A Smartphone-Based Skin Disease Classification Using MobileNet CNN
This work addresses skin disease diagnosis for mobile users, but it is incremental as it applies standard methods to a specific dataset.
The researchers tackled skin disease classification using a MobileNet CNN on an imbalanced dataset of 3,406 images, achieving up to 94.4% accuracy by applying oversampling and data augmentation techniques, and deployed the model in an Android application.
The MobileNet model was used by applying transfer learning on the 7 skin diseases to create a skin disease classification system on Android application. The proponents gathered a total of 3,406 images and it is considered as imbalanced dataset because of the unequal number of images on its classes. Using different sampling method and preprocessing of input data was explored to further improved the accuracy of the MobileNet. Using under-sampling method and the default preprocessing of input data achieved an 84.28% accuracy. While, using imbalanced dataset and default preprocessing of input data achieved a 93.6% accuracy. Then, researchers explored oversampling the dataset and the model attained a 91.8% accuracy. Lastly, by using oversampling technique and data augmentation on preprocessing the input data provide a 94.4% accuracy and this model was deployed on the developed Android application.