# A Smartphone-Based Skin Disease Classification Using MobileNet CNN

arXiv: https://arxiv.org/abs/1911.07929 · HTML: https://mlsanity.com/paper/1911.07929/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning](https://mlsanity.com/paper/2202.07470/) (2022)
- [Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets](https://mlsanity.com/paper/2204.02275/) (2022)
- [Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis](https://mlsanity.com/paper/2208.11278/) (2022)
- [Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization](https://mlsanity.com/paper/2208.11187/) (2022)
- [MpoxSLDNet: A Novel CNN Model for Detecting Monkeypox Lesions and Performance Comparison with Pre-trained Models](https://mlsanity.com/paper/2405.21016/) (2024)
- [Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI](https://mlsanity.com/paper/2407.14757/) (2024)

## Abstract (arXiv)

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.