A Systematic Literature Review of Deep Learning Models for Medical Image Analysis

Abstract

Medical image analysis has witnessed substantial transformation through the application of deep learning models, yet the rapid proliferation of research in this domain poses significant challenges for synthesizing coherent insights. Our objective in this systematic literature review is to comprehensively map the landscape of deep learning approaches applied to medical imaging, with a focus on architectural innovations, task-specific solutions, learning paradigms, and emerging methodological trends. We conducted a structured review following established guidelines for systematic literature synthesis. The methodology involved a multi-stage screening process to identify relevant studies, followed by thematic categorization across eight dimensions, including model design, core tasks, data efficiency, explainability, clinical applications, pre-processing, emerging trends, and systemic challenges. Our analysis reveals that convolutional neural networks remain foundational, though transformer-based architectures and hybrid models are increasingly prevalent for tasks such as segmentation, classification, and detection. Data efficiency techniques, including self-supervised and few-shot learning, have become critical to address the scarcity of annotated medical datasets. We also observe a growing emphasis on explainability and uncertainty quantification to foster clinical trust, alongside rising concerns about privacy-preserving training and federated learning. The review further identifies persistent gaps, particularly in the validation of models across diverse populations and imaging modalities. We conclude that while deep learning has achieved remarkable performance in many medical imaging benchmarks, substantial work remains to ensure generalization, interpretability, and ethical deployment in real-world clinical settings. This systematic review provides a structured reference for researchers and practitioners navigating this interdisciplinary field.