Literature review: efficient deep neural networks techniques for medical image analysis

Abdou, Mohamed A.

doi:10.1007/s00521-022-06960-9

Cited by 139 publications

(68 citation statements)

References 67 publications

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“…One of the core abilities of deep learning is that they learn features automatically from raw data instead of extracting hand-crafted features from data by the user. In medical sector, deep learning techniques are providing good solutions for diagnosing medical images and it provides an assist for medical experts to interpret and diagnose medical images [ 7 – 9 ]. There are numerous deep learning algorithms in the literature for diagnosing from CT images.…”

Section: Related Workmentioning

confidence: 99%

CTSC-Net: an effectual CT slice classification network to categorize organ and non-organ slices from a 3-D CT image

Nithiyaraj¹,

Arivazhagan²

2022

Neural Comput & Applic

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Computed tomography (CT) is a non-invasive diagnostic imaging modality that reveals more insight into human organs than conventional X-rays. In general, the CT output is a 3-D image that is formed by combining multiple 2D images or slices together. It is essential to keep in mind that not all of the slices provide significant information to detect tumours. Usually, a 3-D CT image obtained from the CT scanners has a significant number of unwanted non-organ slices in it. Radiologists typically devote a significant amount of time to select the slices with organ from a 3-D CT image. The presence of a tumour is only evident in the organ slice; hence, radiologists must be cautious not to skip any organ slices. This work is evaluated on the LITS, 3DIRCADb and COVID-19 CT datasets. The three datasets collectively contain 22,435 organ slices and 53,661 non-organ slices, and there is a huge gap between the number of organ and non-organ slices. There is a need for the automatic elimination of non-organ slices in 3-D CT volumes to assist the physicians, and hence, this work focuses on the automatic recognition of organ slices from 3-D CT volumes. In this paper, a new deep model called the computed tomography slice classification network (CTSC-Net) is proposed for CT slice classification between organ and non-organ slices. The model is trained on 77,980 CT slices, validated on 9748 slices and tested on 12,571 slices. Nine CNN architectures with different layer settings are trained and tested to arrive at the final optimal model. The performance measures are computed in terms of true positive rate, true negative rate, sensitivity, specificity and accuracy. The 20-layer CTSC-Net achieves a validation accuracy of 95.04% and an overall testing accuracy of 99.96%. The proposed model is compared to eight different pre-trained CNN models, and the results of the proposed CTSC-Net surpassed all the comparable models. The activation feature maps of different layers of the CTSC-Net are visualized to verify the discriminative features learned by the network. Hence, the proposed CTSC-Net can be employed as a computer-aided diagnosis tool to help physicians discard unnecessary non-organ slices from the 3-D CT volume and to speed up the CT diagnosis process.

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Section: Related Workmentioning

confidence: 99%

CTSC-Net: an effectual CT slice classification network to categorize organ and non-organ slices from a 3-D CT image

Nithiyaraj¹,

Arivazhagan²

2022

Neural Comput & Applic

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“…The main motivation for reviewing and explaining how artificial neural networks (NNs) and deep generative models work in medical imaging is to encourage its use in medical works. Surveys and reviews as those of authors like Akazawa and Hashimoto [ 1 ], De Siqueira et al [ 2 ], Fernando et al [ 3 ], Chen et al [ 4 ], Sah and Direkoglu [ 5 ], and Abdou [ 6 ] cover a significant amount of works that apply deep learning to medical image analysis. Regarding generative models, Zhai et al [ 7 ] review numerous autoencoder variants, while Kazeminia et al [ 8 ] focus on the application of GANs for medical image analysis.…”

Section: Introductionmentioning

confidence: 99%

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Celard

Iglesias

Sorribes-Fdez

et al. 2022

Neural Comput & Applic

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Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.

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“…Expert systems in medicine provide objective, cost-free, and high-speed diagnostic opportunities compared to medical diagnosis methods. In particular, with the development of algorithms such as convolutional neural networks (CNNs) [ 1 ], deep auto-encoders (DAEs) [ 2 ], and generative adversarial networks (GANs) [ 3 ] from DL methods, new methods in medical data processing have been discovered. The use of these methods compared to traditional algorithms in medical data processing has accelerated the application and calculation time of techniques such as feature extraction [ 4 ], classification [ 5 ], and segmentation [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

et al. 2022

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It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery.

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Literature review: efficient deep neural networks techniques for medical image analysis

Cited by 139 publications

References 67 publications

CTSC-Net: an effectual CT slice classification network to categorize organ and non-organ slices from a 3-D CT image

CTSC-Net: an effectual CT slice classification network to categorize organ and non-organ slices from a 3-D CT image

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

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