Vision-based personalized Wireless Capsule Endoscopy for smart healthcare: Taxonomy, literature review, opportunities and challenges

Muhammad, Khan; Khan, Salman; Kumar, Neeraj; Ser, Javier Del; Mirjalili, Seyedali

doi:10.1016/j.future.2020.06.048

Cited by 42 publications

(14 citation statements)

References 107 publications

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“…The experimental results confirm the faster retrieval of our framework and the fine quality of exactness in finding video from huge repository. In future, we aim to use medical and healthcare data [41] for efficient endoscopy video retrieval [42]and its sub-domains, functional in IoT environments [43].…”

Section: Discussionmentioning

confidence: 99%

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

et al. 2020

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The extensive video surveillance networks gather an enormous amount of data exponentially on a daily basis and its management is a challenging task, requiring efficient and effective techniques for searching, indexing, and retrieval. The employed mainstream techniques are focusing on general category videos, where the important events in surveillance require fine-grained events retrieval. In this paper, we introduce an event-oriented feature selection mechanism by utilizing the intermediate convolutional layer of a pre-trained 3D-CNN model, that is selected after deep investigation of its weights and response to a particular event. The extracted exclusive features represent an event semantically and effectively eliminate those neurons which do not respond to that event. Furthermore, the event-oriented convolutional features are of very high-dimensions, requiring additional storage, and take more time in feature comparison for retrieval. Therefore, we generate compact binary codes from these features using principle component analysis (PCA) algorithm. This makes our system more efficient to retrieve videos from large scale database. We evaluated our approach on the challenging events of UCF101 and HMDB51 datasets for original features and generated compact codes, achieving faster execution time, and better precision and recall scores. INDEX TERMS Deep learning, feature selection, video retrieval, video analytics, hash codes, surveillance event analysis.

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Section: Discussionmentioning

confidence: 99%

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

et al. 2020

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“…Much work has been devoted in developing an automated approach for ulcer detection [18][19][20][21][22]. Some latest existing techniques are discussed in this section.…”

Section: Related Workmentioning

confidence: 99%

“…Hand-crafted features are selected in classical approaches, whereas deep learning methods can learn to extract informative features in the pipeline. Although, a considerable amount of work has been done in this domain, while accurate stomach lesions detection is still a challenging [18]. This research work is based on two phases, in Phase-I deeplabv3 is utilized as a base model of the pretrained ResNet-50 model to overcome the existing limitations.…”

Section: Introductionmentioning

confidence: 99%

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3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

Amin

Sharif

Gul

et al. 2021

Complex Intell. Syst.

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Wireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.

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“…Block-chain based approaches have also been proposed for edge based healthcare monitoring [7]. Use of deep learning based image analysis has shown tremendous impact in classic medical problems like brain tumor classification [66] and endoscopy [67]. The approaches which use video as an input, multiple operations are involved for vital signs extraction which are shown as a basic framework given in Fig.…”

Section: Introductionmentioning

confidence: 99%

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

et al. 2021

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The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques.

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Vision-based personalized Wireless Capsule Endoscopy for smart healthcare: Taxonomy, literature review, opportunities and challenges

Cited by 42 publications

References 107 publications

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

Event-Oriented 3D Convolutional Features Selection and Hash Codes Generation Using PCA for Video Retrieval

3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

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