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Papers on “artificial intelligence deep learning medical diagnosis imaging”

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  2. Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

    Mohammad Behdad Jamshidi, Ali Lalbakhsh, Jakub Talla, et al. · 2020 · IEEE Access · 550 cites

    COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated b

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  3. Deep Learning for Medical Image-Based Cancer Diagnosis

    Xiaoyan Jiang, Zuojin Hu, Shuihua Wang‎, et al. · 2023 · Cancers · 346 cites

    (1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET),

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  4. Artificial intelligence in medical imaging of the liver

    Liqiang Zhou, Jia‐Yu Wang, Song-Yuan Yu, et al. · 2019 · World Journal of Gastroenterology · 262 cites

    Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep

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  5. Use of artificial intelligence and deep learning in fetal ultrasound imaging

    Ruben Ramirez Zegarra, Tullio Ghi · 2022 · Ultrasound in Obstetrics and Gynecology · 99 cites

    Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep-learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep-learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal

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  6. Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification

    Rawan Ghnemat, Sawsan Alodibat, Qasem Abu Al-haija · 2023 · Journal of Imaging · 67 cites

    Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneum

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  7. Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis

    Li Yan, Qing Li, Kang Fu, et al. · 2025 · Bioengineering · 64 cites

    The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across va

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