The need for continued innovation in explainable AI, computational efficiency, and regulatory compliance to bridge the gap between theoretical advancements and clinical implementation is underscored.
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.
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
With all its innovations, artificial intelligence (AI) has enabled unprecedented breakthroughs in many sectors such as medical image analysis, which may involve diagnosis, and treatment planning. Segmentation of medical images is an essential operation within this framework since it makes possible the identification of organs, tumors, and pathological regions within the image. However, although this is one of the most important aspects of medical image processing, segmentation still continues to pose some challenges. Traditional segmentation techniques struggle to segment complex medical images, especially in the presence of noise, intensity variations, and unclear boundaries. For this reason, Deep learning becomes the primary solution to overcome the limitations that hinder segmentation performance. In this overview, recently proposed supervised deep learning methods of medical image segmentation, including CNN-based, Transformer-based, and hybrid models, focusing on selected works related to breast and brain tumor segmentation and compares them according to model family, and reported performance. The paper also discusses current challenges and future research directions for developing more reliable and clinically useful segmentation models.
Soumia Azougagh, Abdelmajid Badri, Ilham el Mourabit· IEEE International Conferenc...· 0 citations
It is concluded 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 clinicalsettings.
Govinda Sahu· Journal of Machine Learning...· 0 citations
The integration of artificial neural networks (ANNs), particularly convolutional neural networks (CNNs), into medical imaging has fundamentally transformed the paradigm of early disease diagnosis. This comprehensive review systematically examines the architecture, functionality, and clinical effectiveness of ANN-based models applied to a diverse spectrum of medical imaging modalities, encompassing mammography, magnetic resonance imaging (MRI), computed tomography (CT), fundus photography, and dermoscopy. Evidence synthesized from the current literature demonstrates that state-of-the-art deep learning architectures—including ResNet, VGG, DenseNet, InceptionV3, and U-Net—consistently achieve diagnostic accuracy exceeding 93% across oncological, neurological, ophthalmological, and pulmonary applications. Noteworthy findings include a 98.2% classification accuracy for brain tumor MRI analysis, 97.4% for breast cancer mammographic detection, and area under the receiver operating characteristic curve (AUC) values surpassing 0.97 in multi-class diagnostic tasks. The paper further critically evaluates the comparative performance of leading architectures through structured tables and graphical analyses, highlights the role of transfer learning in addressing data scarcity, and identifies persistent challenges including model interpretability, data heterogeneity, class imbalance, and regulatory compliance barriers. Future directions toward federated learning, explainable AI (XAI), and multimodal fusion frameworks are discussed as pathways to clinically deployable ANN-based diagnostic tools.
Maryam Omar Al-Tohamy, Abdel Hamid, A. Arjiah et al.· Al-Farooq Journal of Science...· 0 citations
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.
Jay Kumar Pandey, S. K. Verma, J. Kumar et al.· Seminars in ultrasound, CT,...· 1 citation
Despite the utilization of multimodal medical imaging as supplementary anatomical and functional data crucial for precise illness diagnosis, the appropriate integration of multimodal pictures has been challenging due to discrepancies in resolution, contrast, and disease-specific imaging characteristics. Most established techniques for image fusion are modality-specific, depend on manually crafted features, and lack the adaptability to encompass a wide variety of pathological presentations, hence constraining their clinical applicability. This paper proposes an AI-driven, disease-specific, adaptive multimodal medical image fusion utilizing Convolutional Neural Networks (CNNs) to tackle these challenges. This strategy is proposed to be an end-to-end trained modality-specific and pathology-aware feature capable of generating adaptive fusion to increase clinically significant areas, excluding structural characteristics. The qualitative and quantitative assessments reveal that the experimental results of multimodal medical image datasets indicate that the proposed method surpasses both traditional and sophisticated deep learning-based fusion techniques. The performance metrics of PSNR, SSIM, entropy, and mutual information, which indicate enhanced fusion quality, contrast, and diagnostic clarity, substantiate the effectiveness of the proposed framework in facilitating disease-oriented clinical decision-making and computer-aided diagnostics systems.
K. Jameema, T. Sunitha, Maruturi.Haribabu et al.· 2026 4th International Confe...· 0 citations