Jul 2026· Journal of Artificial Intelligence and Information· Vol 8, pp. 425-427· 0 citations· 14 references
TL;DR
This paper presents a comprehensive analysis of deep learning applications in medical imaging analysis, highlighting the critical role of artificial intelligence in modern healthcare diagnostics and underscore the necessity for modern medical imaging systems to incorporate sophisticated deep learning techniques to effectively handle the complexity of disease detection and diagnosis.
Abstract
This paper presents a comprehensive analysis of deep learning applications in medical imaging analysis. We examine the evolution of medical image processing from traditional computer vision approaches to sophisticated deep learning solutions, highlighting the critical role of artificial intelligence in modern healthcare diagnostics. Our research encompasses multiple dimensions of medical imaging analysis, including disease detection, segmentation, classification, and automated diagnosis across various imaging modalities. Through extensive evaluation across diverse medical imaging datasets, we demonstrate significant improvements in diagnostic accuracy and efficiency. Our findings show that deep learning techniques can achieve 95% accuracy in disease detection, a 40% reduction in analysis time, 85% improvement in early diagnosis, 30% reduction in false positives, and 99.9% reproducibility in results. Key contributions include the development of novel deep learning architectures, integration of multi-modal image analysis, implementation of real-time diagnostic systems, and establishment of comprehensive validation frameworks. Our findings underscore the necessity for modern medical imaging systems to incorporate sophisticated deep learning techniques to effectively handle the complexity of disease detection and diagnosis.
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
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
The progress in medical imaging technology, including Magnetic Resonance Imaging (MRI), has greatly aided in the prompt identification and diagnosis of brain cancers. This study article provides a comprehensive examination of the use of machine learning methods to evaluate the seriousness of brain tumors using MRI images. The project seeks to assess the latest approaches, problems, and prospects in using machine learning algorithms to improve the precision and effectiveness of tumor severity diagnosis. The review involves a comprehensive examination of several machine learning techniques, encompassing both conventional methods and current breakthroughs like deep learning models, within the specific context of evaluating the severity of brain tumors. The study investigates the use of several strategies for extracting features, preprocessing data, and using classification algorithms to distinguish between benign and malignant tumors. In addition, the research examines the combination of many types of imaging data and the inclusion of clinical information to enhance the overall prediction accuracy. In addition, this study presents a thorough analysis of the current datasets, benchmarking methods, and assessment criteria to offer valuable insights into the dependability and applicability of the suggested models. The article discusses the difficulties related to having a restricted number of annotated datasets, the comprehensibility of intricate models, and the moral concerns when implementing machine learning in healthcare environments. The survey ends with a discussion on possible future research approaches, highlighting the need for joint endeavors among medical practitioners, image analysts, and machine learning specialists to create strong and practical models for clinical use. The results of this study are anticipated to aid in the progress of medical image analysis and promote the creation of more precise and dependable methods for identifying and describing brain cancers.
D. Rani, Anjaiah Adepu· 2026 4th International Confe...· 0 citations
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.
Maryam Omar Al-Tohamy, Abdel Hamid, A. Arjiah et al.· Al-Farooq Journal of Science...· 0 citations
Artificial intelligence (AI) and deep learning (DL) technologies have revolutionized medical imaging and diagnostics. This comprehensive review synthesizes current evidence on the application, diagnostic accuracy, challenges, and future directions of AI algorithms in ultrasound (US) imaging. We conducted a systematic analysis of peer-reviewed literature examining diagnostic accuracy of deep learning in medical imaging and specific AI applications in ultrasound-guided procedures, breast US diagnosis, AI-based radiomics, and regional anesthesia guidance. Our findings demonstrate that AI algorithms achieve high diagnostic accuracy across multiple ultrasound applications, with sensitivity and specificity often comparable to or exceeding experienced radiologists. In ophthalmology imaging, AI achieved area under curve (AUC) of 0.939-0.969 for various retinal pathologies. In breast ultrasound, AI systems demonstrated sensitivity of 92.5% and accuracy of 78.6% for malignant lesion detection. Deep learning radiomics achieved AUC of 0.978 in pancreatic adenocarcinoma diagnosis and 0.97 in breast cancer characterization. However, significant challenges remain including standardization of training datasets, external validation, clinical workflow integration, regulatory compliance, and reimbursement issues. This review highlights that while AI shows tremendous promise for enhancing diagnostic accuracy and clinical efficiency in ultrasound medicine, successful implementation requires multidisciplinary collaboration, robust validation frameworks, and organizational infrastructure for sustainable clinical integration.
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