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A holistic overview of deep learning approach in medical imaging

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Abstract

Medical images are a rich source of invaluable necessary information used by clinicians. Recent technologies have introduced many advancements for exploiting the most of this information and use it to generate better analysis. Deep learning (DL) techniques have been empowered in medical images analysis using computer-assisted imaging contexts and presenting a lot of solutions and improvements while analyzing these images by radiologists and other specialists. In this paper, we present a survey of DL techniques used for variety of tasks along with the different medical image’s modalities to provide critical review of the recent developments in this direction. We have organized our paper to provide significant contribution of deep leaning traits and learn its concepts, which is in turn helpful for non-expert in medical society. Then, we present several applications of deep learning (e.g., segmentation, classification, detection, etc.) which are commonly used for clinical purposes for different anatomical site, and we also present the main key terms for DL attributes like basic architecture, data augmentation, transfer learning, and feature selection methods. Medical images as inputs to deep learning architectures will be the mainstream in the coming years, and novel DL techniques are predicted to be the core of medical images analysis. We conclude our paper by addressing some research challenges and the suggested solutions for them found in literature, and also future promises and directions for further developments.

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  1. Yogananda School of AI Computer and Data Sciences, Shoolini University, Solan, 173229, Himachal Pradesh, India

    Rammah Yousef & Gaurav Gupta

  2. Electronics and Communication Engineering, Marwadi University, Rajkot, Gujrat, India

    Nabhan Yousef

  3. Jawaharlal Nehru University, New Delhi, India

    Manju Khari

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Yousef, R., Gupta, G., Yousef, N. et al. A holistic overview of deep learning approach in medical imaging. Multimedia Systems 28, 881–914 (2022). https://doi.org/10.1007/s00530-021-00884-5

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