Jul 2026· Seminars in ultrasound, CT, and MR· 1 citation· 82 references
Medicine
TL;DR
Key deep learning frameworks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) are examined for their applications in important medical imaging tasks such as image classification, segmentation, reconstruction, and disease prediction.
Abstract
Medical imaging plays a crucial role in modern diagnostic practices, but traditional techniques often face limitations in accuracy, efficiency, and scalability. The emergence of deep learning (DL) has led to significant improvements that are transforming this field. This review discusses how DL algorithms are enhancing diagnostic imaging by improving accuracy, enabling automated analysis, and supporting personalized treatment plans. It focuses on key deep learning (DL) frameworks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The review examines their applications in important medical imaging tasks such as image classification, segmentation, reconstruction, and disease prediction. It also considers how DL techniques are integrated with tools like radiomics, data augmentation strategies, and predictive analytics models. DL methods have shown superior performance in detecting and classifying diseases like pneumonia, tuberculosis, and Alzheimer's. They also improve the quality and speed of imaging modalities such as MRI, CT, and ultrasound. Despite these advances, challenges remain in data availability, model interpretability, clinical validation, and ethical issues related to bias and privacy. Addressing these challenges is essential for the successful clinical use of DL in medical imaging. This review ends with suggestions for future directions and best practices for ethically and practically integrating DL technologies into routine healthcare.
Empirical evidence from recent studies demonstrates that fine-tuning and network-based DTL strategies, including federated learning, consistently enhance diagnostic accuracy, robustness, and generalization across multiple medical imaging modalities, particularly in data-limited clinical scenarios.
M. A. S. Banu, A. Dhavapandiammal, K. Palanisamy· Current medical imaging· 0 citations
Brain tumor (BT) classification plays a vital role in computer-aided diagnosis, as early detection directly influences treatment planning and patient survival. Advances in artificial intelligence, particularly in machine learning (ML) and deep learning (DL), have greatly improved automated tumor identification from magnetic resonance imaging (MRI), reducing reliance on manual assessment. This study proposes a hybrid framework that combines generative adversarial networks (GANs), convolutional neural networks (CNNs), and Vision Transformers (ViTs) for accurate tumor classification. GANs are used to generate realistic synthetic MRI images, addressing data scarcity and increasing dataset diversity. CNNs extract discriminative deep features, accelerating training while limiting overfitting. These features are then processed by a ViT model, capable of capturing complex spatial relationships within medical images. Experimental results show that the proposed ViT–CNN–GAN approach achieves 97.36% accuracy, outperforming conventional methods. Overall, this framework demonstrates the potential of advanced DL models to strengthen MRI-based tumor diagnosis and support more reliable clinical decision-making.
Iliass Zine-dine, J. Riffi, Khalid El Fazazy et al.· International Journal of Onl...· 0 citations
Deep learning has revolutionized medical image analysis, playing a vital role in modern clinical applications. However, the deployment of large-scale models in real-world clinical settings remains challenging due to high computational costs, latency constraints, and patient data privacy concerns associated with cloud-based processing. To address these bottlenecks, this review provides a comprehensive synthesis of efficient and lightweight deep learning architectures specifically tailored for the medical domain. We categorize the landscape of modern efficient models into three primary streams: Convolutional Neural Networks (CNNs) for local inductive bias, Lightweight Transformers for global context under constrained attention, and emerging Linear Complexity Models for scalable global context. Furthermore, we examine key model compression strategies (including pruning, quantization, knowledge distillation, and low-rank factorization) and evaluate their efficacy in maintaining diagnostic performance while reducing hardware requirements. By identifying current limitations and discussing the transition toward on-device intelligence, this review serves as a roadmap for researchers and practitioners aiming to bridge the gap between high-performance AI and resource-constrained clinical environments.
C. M. Nguyen, Truong-Son Hy· Discover Artificial Intellig...· 0 citations
Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable and accessible medical image analysis.
Lakshmi Sai Anusha Dadi, Pravallika Devi Kommana· International Journal for Re...· 0 citations
Chronic disease early and accurate detection is a major healthcare issue nowadays, and most importantly, there is the rising prevalence or use of heterogeneous medical image data such as CT, MRI, X-ray, and retinal scans. Conventional models of deep learning such as CNNs perform well on spatial aspects of feature extraction but not generally on long-term relations and overall context. In this paper, we introduce a new hybrid deep learning network combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to carry out multi-modal medical image fusion to assist in the diagnostic process with better results. The CNN branch gives fine-grain local representation and the Transformer module will predict the global connections between modalities. Empirical tests on publicly available data show that the proposed model is better than single CNN and Transformer models in using the datasets and there are massive increments in precise score, recall, and F1-score on chronic disease diagnosis in the initial stages. The field of research shows how hybrid architecture can be used to combine complementary information about various scanning modalities, which can become the direction of AI-aided decision-making in prevention.
N.R Azhakeshwari, S. Christy, S. Saranya et al.· 2026 11th International Conf...· 0 citations
The continued integration of AI has the potential to improve diagnostic accuracy, streamline radiological workflows, and support more personalized patient care in the next generation of diagnostic imaging.
Mr. Vishal Walia, Mr. Honey Thakur, Ms. Ashwarya Sharma et al.· PAIN, JOINTS, SPINE· 0 citations