Optimization of Medical Image Analysis Models for Effective Disease Diagnosis through Data Augmentation Techniques

Abstract

Medical imaging plays a pivotal role in contemporary healthcare, offering detailed visualizations essential for disease detection, treatment planning and monitoring. The advent of Deep Learning (DL) has revolutionized medical image analysis by enabling the autonomous detection and classification of abnormalities with high accuracy. However, challenges such as data scarcity, model interpretability and integration into clinical workflows persist. This study presents the development and optimization of a Medical Image Analysis Model (MIAM) aimed at enhancing disease diagnosis through advanced data augmentation techniques. Utilizing a combination of Agile and Iterative development methodologies, the research incorporates State-Of-The-Art (SOTA) techniques including 3D data augmentation and intensity-based augmentation within a Convolutional Neural Network (CNN) framework. These augmentations enrich the training dataset, enabling the model to learn invariant features and improve resilience to variations in imaging conditions. Additionally, the model employs sophisticated loss functions like dice loss for segmentation tasks and robust regularization methods such as dropout, L2 regularization and batch normalization to prevent overfitting and enhance generalizability. The optimized MIAM was rigorously evaluated across multiple medical imaging modalities, including MRI, CT scans and X-rays, targeting diseases such as pneumonia, breast cancer, heart disease, diabetic retinopathy and intracranial hemorrhage. Comparative analyses demonstrated that the optimized CNN model significantly outperformed baseline models, achieving up to a 7.3% increase in classification accuracy and a 7.0% improvement in Dice Similarity Coefficient (DSC) for segmentation tasks. Receiver Operating Characteristic (ROC) curves further validated the model's superior discriminative ability with higher Area Under the Curve (AUC) values. These enhancements are attributed to the integration of advanced augmentation strategies, optimized loss functions and robust regularization techniques, which collectively mitigate overfitting and enhance the model's ability to generalize to unseen data. This study underscores the critical role of data augmentation and regularization in developing high-performing MIAMs. The optimized model's superior performance across various diseases and imaging modalities highlights its potential for clinical integration, promising improved diagnostic accuracy, reduced workloads for healthcare professionals and enhanced patient outcomes. Future research will focus on refining explainable AI strategies, integrating multi-modal data and validating clinical efficacy through prospective trials, thereby advancing personalized healthcare through reliable and efficient AI-driven medical image analysis.