过采样
预处理器
计算机科学
数据预处理
人工智能
Android(操作系统)
分类器(UML)
学习迁移
数据挖掘
模式识别(心理学)
机器学习
带宽(计算)
操作系统
计算机网络
作者
Jessica S. Velasco,Cherry G. Pascion,Jean Wilmar Alberio,Jonathan Apuang,John Stephen Cruz,Mark Angelo Gomez,Benjamin Jr. Molina,Lyndon Tuala,August C. Thio-ac,Romeo Jr. L. Jorda
出处
期刊:International journal of advanced trends in computer science and engineering
[The World Academy of Research in Science and Engineering]
日期:2019-10-15
卷期号:: 2632-2637
被引量:47
标识
DOI:10.30534/ijatcse/2019/116852019
摘要
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.
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