Medical Image Analysis using Convolutional Neural Networks: A Review

By Adnan Qayyum, Syed Muhammad Anwar, Muhammad Majid, Muhammad Awais, Majdi R. Alnowami. Journal of medical systems, 2017

1,280 citations recorded by the source corpus.

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Subfields: Information Systems, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4ec06dda674cd5f93351f0ab5871391671f9b974

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Recent advances and clinical applications of deep learning in medical image analysis (2021, methodology): “A simple CNN for disease classification from MRI images (Anwar et al., 2018).”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, methodology): “A typology of commonly used medical imaging modalities (adapted from [41]).”
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification (2018, background): “Recent surveys [29], [30], [31], [32] have demonstrated that deep learning technologies have been extensively applied to the ﬁeld of chest X-ray image annotation [33], classiﬁcation [6], [34], [8], [9], and detection (localization) [35], [36].”
Segment Anything Model for medical image analysis: an experimental study (2023, background): “Image segmentation is a central task in medical image analysis, ranging from the segmentation of organs [17], abnormalities [3], bones [8], and others [13].”
MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation (2019, background): “Never-theless, CNNs have shown great promise in medical image segmentation in recent years [22, 23], and most of the credit go to U-Net [24].”
What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine (2023, methodology): “CNN models analyse receptive fields with filters [58].”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, positive): “As an example, in the case of data-centric studies such as computer vision, DL methods have proved to be extremely successful in providing optimal solutions (Anwar et al., 2018; Liu et al., 2018).”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “In this regard, researchers are developing learning-based methods using such large-scale datasets, particularly, DL-based methods have provided the state-of-the-art performance in many medical image analysis tasks [168].”