A Smartphone-Based Skin Disease Classification Using MobileNet CNN
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Nov 2019.
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Citing papers per year: 2022: 4; 2023: 0; 2024: 2.
6 papers.
- 2022Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learningcited by 21
- 2022Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasetscited by 6
- 2022Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosiscited by 5
- 2022Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalizationcited by 4
- 2024MpoxSLDNet: A Novel CNN Model for Detecting Monkeypox Lesions and Performance Comparison with Pre-trained Modelscited by 1
- 2024Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AIcited by 1
- 2024Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI20 Jul 2024
- 2024MpoxSLDNet: A Novel CNN Model for Detecting Monkeypox Lesions and Performance Comparison with Pre-trained Models31 May 2024
- 2022Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis24 Aug 2022
- 2022Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization23 Aug 2022
- 2022Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets3 Apr 2022
- 2022Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning14 Feb 2022