A Mini-review on CNN-based Biomedical Image Processing: Modality, Applications, and Future Trends

Abstract

Convolutional Neural Networks (CNNs) have emerged as a transformative technology in biomedical image processing with unprecedented capabilities in automatically identifying key patterns (feature extraction). This review systematically examines the current state of CNNbased medical image processing, focusing on its applications, limitations, and future directions. We analyze studies employing 2D, 3D, and multimodal CNN architectures for critical tasks such as disease detection, segmentation, and registration. The review highlights the adaptability of CNNs across diverse data modalities, including visual (e.g., histopathology, X-rays), spectral (e.g., spectroscopy), textual (e.g., sentiment analysis), volumetric (e.g., MRI, CT), and hybrid datasets. Despite their potential, challenges such as data heterogeneity, model interpretability, and integration into clinical workflows hinder widespread adoption. Key applications include cancer detection (e.g., breast, lung, colon), cardiovascular and neurological disorder diagnosis, and ophthalmological disease classification. Advanced segmentation techniques (e.g., instance, semantic, multi-class) and registration methods (e.g., rigid, deformable, multi-modal) are explored, emphasizing CNN-driven innovations. The review also addresses preprocessing techniques, model interpretability, and the integration of CNNs with Clinical Decision Support Systems (CDSS). Emerging trends such as generative AI, real-time processing, and federated learning are discussed as potential solutions to current limitations. By synthesizing these insights, this review serves as a roadmap for researchers and clinicians aiming to harness CNNs for transformative healthcare outcomes. It underscores the need for continued innovation in explainable AI, computational efficiency, and regulatory compliance to bridge the gap between theoretical advancements and clinical implementation.