Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review

Khan, Wasif; Zaki, Nazar; Ali, Luqman

doi:10.1101/2020.07.09.20150342

Cited by 14 publications

(7 citation statements)

References 75 publications

(137 reference statements)

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“…Though the values of all metrics for the image classifier (see Table) are relatively poor compared to the report classifier, they entirely correspond to the current world level [58][59][60]. Note that during training the report classifier, only obviously correct text reports were used; in reality, it is necessary to consider the human factor associated with errors and fatigue of radiologists, which reduces the received metrics.…”

Section: Resultsmentioning

confidence: 99%

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2020

ICS

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Section: Resultsmentioning

confidence: 99%

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2020

ICS

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“…We developed a DL model to predict the cognitive gap. Over the past decade, DL techniques have shown an enormous breakthrough in the field of medical diagnostics by achieving excellent performance in automatic medical imaging classification, detection, and segmentation [25].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning for Predicting Cognitive Gap as a Reliable Biomarker of Dementia

Habuza

Zaki

Statsenko

et al. 2021

Preprint

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Neuroimaging data may reflect the mental status of both cognitively preserved individuals and patients with neurodegenerative diseases. To find the relationship between cognitive performance and the difference between predicted and observed functional test results, we developed a Convolutional Neural Network (CNN) based regression model to estimate the level of cognitive decline from preprocessed T1-weighted MRI images. In this study, we considered the Predicted Cognitive Gap (PCG) as the biomarker to accurately classify Healthy Control (HC) subjects versus Alzheimer disease (AD) subjects. The proposed model was tested on a dataset that includes 422 HC and 377 AD cases. The performance of the proposed solution was measured using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and achieved 0.987 (ADAS-cog), 0.978 (MMSE), 0.898 (RAVLT), 0.848 (TMT), 0.829 (DSST) for averaged brain images; and 0.985 (ADAS-cog), 0.987 (MMSE), 0.901 (RAVLT), 0.8474 (TMT), 0.796 (DSST) for middle slice skull stripped brain images. The results achieved indicate that PCG can accurately separate healthy subjects from demented ones and thus, the structure of the brain contributes to the level of human cognition and their functional abilities. Therefore, PCG could be used as a biomarker for dementia.

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“…After the SSD many other detection models and techniques like Ratina Net [36] are proposed which are used sometimes by researchers for specific tasks. However, for general detection, Yolo and SSD are still used on a large scale.…”

Section: Ssdmentioning

confidence: 99%

Object Detection in Last Decade - A Survey

Arshad

2021

SJI

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In the last decade, object detection is one of the interesting topics that played an important role in revolutionizing the presentera. Especially when it comes to computervision, object detection is a challenging and most fundamental problem. Researchersin the last decade enhanced object detection and made many advance discoveries using thetechnological advancements. When wetalk about object detection, we also must talk about deep learning and its advancements over the time. This research work describes theadvancements in object detection over last10 years (2010-2020). Different papers published in last 10 years related to objectdetection and its types are discussed with respect to their role in advancement of object detection. This research work also describesdifferent types of object detection, which include text detection, face detection etc. It clearly describes the changes inobject detection techniques over the period of the last 10 years. The Objectdetection is divided into two groups. General detectionand Task based detection. General detection is discussed chronologically and with its different variants while task based detectionincludes many state of the art algorithms and techniques according to tasks. Wealso described the basic comparison of how somealgorithms and techniques have been updated and played a major role in advancements of different fields related to object detection.We conclude that the most important advancements happened in the last decade and the future is promising much more advancement inobject detection on the basis of work done in this decade.In the last decade, object detection is one of the interesting topics that played an important role in revolutionizing the presentera. Especially when it comes to computervision, object detection is the challenging and most fundamental problem. Researchersinlast decade enhanced object detection and made many advance discoveries using thetechnological advancements. When wetalk about object detection, we also must talk about deep learning and its advancements over the time. This research work describes theadvancements in object detection over last10 years (2010-2020). Different papers published in last 10 years related to objectdetection and its types are discussed with respect to their role in advancement of object detection. This research work also describesdifferent types of object detection, which include text detection, face detection etc. It clearly describes the changes inobject detection techniques over the period of last 10 years. The Objectdetection is divided into two groups. General detectionand Task based detection. General detection is discussed chronologically and with its different variants while task based detectionincludes many state of the art algorithms and techniques according to tasks. Wealso described the basic comparison of how somealgorithms and techniques have been updated and played a major role in advancements of different fields related to object detection.We conclude that the most important advancements happened in last decade and future is promising much more advancement inobject detection on the basis of work done in this decade.

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Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review

Cited by 14 publications

References 75 publications

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Deep Learning for Predicting Cognitive Gap as a Reliable Biomarker of Dementia

Object Detection in Last Decade - A Survey

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