Improving Breast Cancer Detection with Upsampling and Resizing Algorithms in Image Processing

Abstract

Our study focuses on the detection of breast cancer using medical image analysis. The researchers explore the effectiveness of various oversampling methods in improving the performance of deep learning models for breast cancer detection. The dataset used in the study has a severe class imbalance with a disproportionate number of cancerous and non-cancerous examples. Six oversampling methods are evaluated in this study. Each oversampling method is applied to the dataset, and the augmented data is used to train deep learning models. The performance of each oversampling method is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that oversampling methods significantly enhance the performance of deep learning models for breast cancer detection. SVM-SMOTE and ADASYN consistently outperform other methods, achieving the highest F1 scores on both ResNet-50 and AlexNet architectures. The findings also suggest that the choice of oversampling method has a substantial impact on model performance, emphasizing the importance of selecting an appropriate oversampling technique for imbalanced data. Overall, this study highlights the significance of addressing class imbalance in medical image analysis and provides valuable insights into the effectiveness of different oversampling methods in improving the performance of deep learning models for breast cancer detection. © 2024 IEEE.