A Review Of Convolutional Neural Networks For Medical Image Analysis: Trends And Future Directions

Abstract

One of the most important parts in diagnosing and monitoring the disease is medical imaging, image data is complex and its interpretation is manual. In this review, Convolutional Neural Networks (CNNs) from the application side and their evolutions are highlighted in the analysis of medical images. Specifically, CNNs are specialized deep learning models based on biological processes that automatically learn features of raw image data. Although compared to traditional methods, CNNs have made a great progress for the applications of medical image classification, segmentation and detection. Due to the architectures like VGG, ResNet and Inception, CNNs were enhanced and now capable of disease detection, organ segmentation, and classification of tumors quite accurately. All these advances come with challenges like lack of data availability, complexity or interpretability of models and computational resources. These limitations have sparked transfer learning and fine-tune pre trained models approach, as well as amalgamation of multi modal data. Explainable AI (XAI) come into frequent use for future model transparency and clinical trust. That leads to future directions such as improving generalization, robustness and integration of the model to Electronic Health Records (EHRs) for personalised treatment insight. Solving these challenges will further solidify CNNs as key transformative technology in medical image study for accurate analytical, treatment plan, and patient outcome.