A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions

Pandey, Babita; Pandey, Devendra Kumar; Mishra, Brijendra Pratap; Rhmann, Wasiur

doi:10.1016/j.jksuci.2021.01.007

Cited by 65 publications

(57 citation statements)

References 154 publications

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“…The DBN (Deep Network of Beliefs), created by Hinton [107], consists of two networks that build each other: of beliefs represented by an acyclic graph composed of layers of stochastic binary units with weighted and respectively weighted connections, restricted Boltzmann Machines which is a stochastic [1]. DBNs are applied in image recognition and speech recognition, in classification to detect lesions in medical diagnosis and, in video recognition to identify the presence of persons [108], in speech recognition to understand missing words in a sentence [109] and in application on physiological signals to recognize human emotion [110].…”

Section: Model Description and Classification According To Medical Data Types Used Objectives And Performances In Medical Applicationsmentioning

confidence: 99%

“…DTN contains a characteristic extraction layer, which teaches a shared feature subspace in which marginal source distributions and target samples are drawn close and a layer [1] of discrimination that match conditional distributions by classified transduction [111]. It is used for large-scale problems [1].…”

Section: Model Description and Classification According To Medical Data Types Used Objectives And Performances In Medical Applicationsmentioning

confidence: 99%

“…The prejudices of the generalisers with regard to the learning set shall be inferred. It works effectively and better than an eco-validation strategy when used with multiple generalisers compared to individual generalisers [1].…”

Section: Model Description and Classification According To Medical Data Types Used Objectives And Performances In Medical Applicationsmentioning

confidence: 99%

“…This paper presents a methodical review of the literature [1] with the objective of carrying out an analysis of the importance of the relationship between the types and characteristics of scientific data and their use of deep learning models in the interpretation of medical images. We have defined a methodology for semiautomating the production of relevant articles and eliminating those with low impact in the scientific community, by applying inclusive and exclusive quality criteria in the fields of medicine and information technology [2].…”

Section: Introductionmentioning

confidence: 99%

“…The types of images used in the analysis of medical images are: CT, MRI, Xray, Ultra-sound, PET, Wave images, Biopsy, Mammography and Spectrography [1]. In the process of images analysis of the tasks of extracting characteristics, reducing size, augmentation, segmentation, grouping or classification are decisive for the efficiency and precision of integration methods [5] [14] [19] [20].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Ursuleanu¹,

Luca²,

Gheorghe³

et al. 2021

ETSN

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Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.

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Section: Model Description and Classification According To Medical Data Types Used Objectives And Performances In Medical Applicationsmentioning

confidence: 99%

Section: Model Description and Classification According To Medical Data Types Used Objectives And Performances In Medical Applicationsmentioning

confidence: 99%

Section: Model Description and Classification According To Medical Data Types Used Objectives And Performances In Medical Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Ursuleanu¹,

Luca²,

Gheorghe³

et al. 2021

ETSN

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An efficient convolutional histogram‐oriented gradients and deep convolutional learning approach for accurate classification of bone cancer

Vijayaraj,

Abirami,

Mohanty

et al. 2023

Int J Imaging Syst Tech

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In our human body bones are the most significant part, which helps people to move and perform several activities. But, the cancer is caused by producing abnormal cell, which is rapidly spread to the whole parts of the body. Bone cancer is one of the critical types due to its malignancy more than other cancers. The approach involves preprocessing and segmentation of input images to remove noise and resize images, followed by feature extraction using a Convolutional histogram of oriented gradients (ConvHisOrGrad). The ROI extraction helps to accurately identify abnormal parts around the cancerous area. The Extreme Deep Convolutional learning machine (Ex‐ConVLM) is used for normal and cancerous bone classification based on the texture properties of bone MRI images. The proposed technique was evaluated using a dataset of 220 bone MRIs for tumor classes classified as necrotic, non‐tumor, and viable‐tumor. Results showed that the proposed technique outperformed existing techniques with the highest accuracy of 97% for the necrotic tumor class, 98.2% for the non‐tumor class, and 98.6% for the viable tumor class. The fine‐tuned model shows promising performance in detecting malignancy in bone based on histological images. In summary, the proposed technique utilizes deep learning architectures and ROI extraction for the accurate identification of abnormal parts in bone MRI images, achieving state‐of‐the‐art performance in the detection and categorization of bone cancers.

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Prediction of Colon Cancer Related Tweets Using Deep Learning Models

Baker

Mohammed

Jihad

2023

Intelligent Systems Design and Applications

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A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions

Cited by 65 publications

References 154 publications

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

An efficient convolutional histogram‐oriented gradients and deep convolutional learning approach for accurate classification of bone cancer

Prediction of Colon Cancer Related Tweets Using Deep Learning Models

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