Improving the Sensitivity of Unbalanced Medical Image Classification by Random Up-sampling Numerical Features
Open Access
- 26 April 2024
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 15-21
Abstract
Deep learning is currently being used for image diagnosis in medicine. If image diagnosis can be performed with high sensitivity using deep learning, it will be possible to reduce the burden on medical specialists reading images. However, abnormal medical images are often difficult to detect by deep learning due to the large number of normal medical images. To improve such unbalanced classification sensitivity, we improved the sensitivity of predicting whether a medical image is benign or malignant by up-sampling the numerical features obtained by radiomics from the image and randomly selecting and averaging them. The unbalanced VinDr-Mammo dataset, with 988 malignant and 5606 benign cases, was used for our study. The sensitivity for deep learning image classification was 0.2626 for CNN, 0.0000 for VisionTransformer, 0.0101 for Radom Forest (radiomics features), 0.0101 for Radom Forest (upscaled radiomics features) was 0.4141. The sensitivity for the ensemble of predictions by CNN and Radom Forest (upscaled radiomics features) was 0.5303. The results suggest the effectiveness of the up-sampled radiomics feature method, which randomly selects and averages features in an unbalanced medical image classification. This research leaves room for investigation using sampling methods such as SMOTE and AdaBoost, as well as research into the robustness and generalizability of the model to diverse datasets.Keywords
This publication has 17 references indexed in Scilit:
- Radiomics analysis in medical imaging researchJournal of Medical Radiation Sciences, 2023
- Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imagingInternational Journal of Computer Assisted Radiology and Surgery, 2020
- Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patientsEuropean Journal of Nuclear Medicine and Molecular Imaging, 2020
- Introduction to RadiomicsJournal of Nuclear Medicine, 2020
- Deep learning for cellular image analysisNature Methods, 2019
- Deep Learning Applications in Medical Image AnalysisIEEE Access, 2017
- Promises and challenges for the implementation of computational medical imaging (radiomics) in oncologyAnnals of Oncology, 2017
- Unbalanced data classification using support vector machines with active learning on scleroderma lung disease patternsJournal of Applied Statistics, 2014
- Learning from Imbalanced DataIEEE Transactions on Knowledge and Data Engineering, 2009
- Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performanceNeural Networks, 2008