Hybrid Approach for Handling Class Imbalance on Medical Data

Class imbalance in medical X-ray image datasets poses a significant challenge for developing accurate machine-learning models. This paper presents a novel “Integrated Strategy for Addressing Class Imbalance in Medical Image Datasets” aiming to tackle this issue systematically. The proposed approach combines weighted loss functions and an ensemble model comprising a pre-trained DenseNet architecture and a customized model. The methodology is applied to a representative medical image dataset, demonstrating its effectiveness in mitigating class imbalance issues. The findings reveal notable improvements in model performance, particularly in underrepresented classes. This research advances robust machine learning models in medical image analysis, with potential applications in medical imaging and illness diagnostics. The results underscore the necessity for hybrid approaches and highlight the efficacy of ensemble models and weighted loss in addressing class imbalance in medical imaging datasets.