A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract

Ali, Hussam; Sharif, Muhammad; Yasmin, Mussarat; Rehmani, Mubashir Husain; Riaz, Farhan

doi:10.1007/s10462-019-09743-2

Cited by 43 publications

(27 citation statements)

References 201 publications

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“…Furthermore, performing a comprehensive colonoscopy of an individual can take from 10 min to several hours while making hundreds, sometimes even thousands of frames. Since not all these frames offer the gastroenterologist useful information, the examination of them is a demanding and elongated task, making the odds of missing them even higher [ 6 ]. Research has proved that if endoscopic diagnose adenoma during colonoscopy, their patient’s risk of having that adenoma turn into colonic cancer reduces significantly.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

et al. 2021

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Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

et al. 2021

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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%

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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“…Detection of anomalies and abnormalities is a challenging field of research employed in many applications, including biomedical [21,22], power generation [23], network traffic [24], cybersecurity [25], and energy consumption [26]. Detecting and analyzing abnormal energy usage patterns in real time can not only promote the process of energy saving, but it can help also tracing appliance failures through analyzing sudden and unexpected changes in energy usage [26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

et al. 2020

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Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.

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A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract

Cited by 43 publications

References 201 publications

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

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

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

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