| Title |
Performance of CNN-based Dermoscopic Image Classifier by using Data Balancing Algorithms |
| Authors |
김거식(Keo Sik Kim) ; 이문섭(Munseob Lee) ; 손동훈(Dong Hoon Son) ; 김정은(Jeong Eun Kim) ; 민기현(Gihyeon Min) ; 김계은(Kyeeun Kim) ; 강현서(Hyun Seo Kang) |
| DOI |
https://doi.org/10.5573/ieie.2020.57.7.76 |
| Keywords |
imbalanced class; deep learning; dermoscopic image; cross validation |
| Abstract |
In order to improve the sensitivity of the minority class without being biased to the majority class, the data balancing algorithms such as ROS, SMOTE, ADASYN, BSMOTE and SVMSMOTE, were applied to imbalanced dataset, HAM10000. It consists of a total of 10,015 dermoscopic images for 7 classes of skin disease with a resolution of 600×450. Then, their performances with and without the data balancing algorithms, including accuracies, sensitivities, precisions, F1-scores. were measured, respectively, and the effect of the data balancing algorithm was verified through 5-fold cross-validation test. Consequently, with applying the data balancing algorithms, the average values of the sensitivities(3.1%∼6.6%), precisions(2.2%∼7.5%) and F1-scores(2.7%∼6.6%) increased significantly (p<0.05). It was shown that they can contribute to the improvement of the performance of the classification of an imbalanced dataset using deep learning model. |