Computer-aided covid-19 patient screening using chest images (X-Ray and CT scans)

Burgos-Artizzu, Xavier P.

doi:10.1101/2020.07.16.20155093

Cited by 5 publications

(3 citation statements)

References 57 publications

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“…Explanation of some acronyms: GT: Ground Truth; MS: Machine Segmentation; TP: True positive; TN: True negative; FP: False positive; FN: False negative; these terms are illustrated in Fig. 7, which has been modified from source [121]. Fig.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

Dao¹,

Ly²

2023

IJACSA

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Over the past decade, Medical Image Segmentation (MIS) using Deep Neural Networks (DNNs) has achieved significant performance improvements and holds great promise for future developments. This paper presents a comprehensive study on MIS based on DNNs. Intelligent Vision Systems are often evaluated based on their output levels, such as Data, Information, Knowledge, Intelligence, and Wisdom (DIKIW), and the state-of-the-art solutions in MIS at these levels are the focus of research. Additionally, Explainable Artificial Intelligence (XAI) has become an important research direction, as it aims to uncover the "black box" nature of previous DNN architectures to meet the requirements of transparency and ethics. The study emphasizes the importance of MIS in disease diagnosis and early detection, particularly for increasing the survival rate of cancer patients through timely diagnosis. XAI and early prediction are considered two important steps in the journey from "intelligence" to "wisdom." Additionally, the paper addresses existing challenges and proposes potential solutions to enhance the efficiency of implementing DNN-based MIS.

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Section: Evaluation Metricsmentioning

confidence: 99%

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

Dao¹,

Ly²

2023

IJACSA

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“…The proposed framework is contrasted with four latest frameworks that is, (Burgos‐Artizzu, 2020; Ewen & Khan, 2020; Wang, Liu, et al, 2020; Yang et al, 2020). The existing published methods classified the COVID‐19 CT images using different pretrained CNN models, these methods achieved a maximum of 0.89 accuracy.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach

Amin

Anjum

Sharif

et al. 2021

Microscopy Res & Technique

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Coronavirus19 is caused due to infection in the respiratory system. It is the type of RNA virus that might infect animal and human species. In the severe stage, it causes pneumonia in human beings. In this research, hand-crafted and deep microscopic features are used to classify lung infection. The proposed work consists of two phases; in phase I, infected lung region is segmented using proposed U-Net deep learning model. The hand-crafted features are extracted such as histogram orientation gradient (HOG), noise to the harmonic ratio (NHr), and segmentation based fractal texture analysis (SFTA) from the segmented image, and optimum features are selected from each feature vector using entropy. In phase II, local binary patterns (LBPs), speeded up robust feature (Surf), and deep learning features are extracted using a pretrained network such as inceptionv3, ResNet101 from the input CT images, and select optimum features based on entropy. Finally, the optimum selected features using entropy are fused in two ways, (i) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are horizontally concatenated/fused (ii) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are combined/fused with deep features. The fused optimum features vector is passed to the ensemble models (Boosted tree, bagged tree, and RUSBoosted tree) in two ways for the COVID19 classification, (i) classification using fused hand-crafted features (ii) classification using fusion of hand-crafted features and deep features. The proposed methodology is tested /evaluated on three benchmark datasets. Two datasets employed for experiments and results show that hand-crafted & deep microscopic feature's fusion provide better results compared to only hand-crafted fused features.

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“…For the STF feature, we implement it in Matlab (given the tracks, available from project website [8]). For DTF, we use the toolbox from [58] under our settings, for example, the trajectory length is set to be 9.…”

Section: Computation Complexitymentioning

confidence: 99%

Human and Animal Behavior Understanding

Chen¹

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Human and Animal Behavior Understanding Wenbin Chen Human and animal behavior understanding is an important yet challenging task in computer vision. It has a variety of real-world applications including human computer interaction (HCI), video surveillance, pharmacology, genetics, etc. We first present an evaluation of spatiotemporal interest point features (STIPs) for depth-based human action recognition, and then propose a framework call TriViews for 3D human action recognition with RGB-D data. Finally, we investigate a new approach for animal behavior recognition based on tracking, video content extraction and data fusion. STIPs features are widely used with good performances for action recognition using the visible light videos. Recently, with the advance of depth imaging technology, a new modality has appeared for human action recognition. It is important to assess the performance and usefulness of the STIPs features for action analysis on the new modality of 3D depth map. Three detectors and six descriptors are combined to form various STIPs features in this thesis. Experiments are conducted on four challenging depth datasets. After evaluating STIPs features for depth-based human action recognition, we propose an effective framework called TriViews to utilize 3D information for human action recognition. It projects the 3D depth maps into three views, i.e., front, side, and top views. Under this framework, five features, i.e., spatiotemporal interest points (STIP), dense trajectory First of all, I would like to express my sincere thanks to my advisor, Dr Guodong Guo for his guidance, encouragement and help during my graduate study as a research assistant. I appreciate his support and trust in me.

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Computer-aided covid-19 patient screening using chest images (X-Ray and CT scans)

Cited by 5 publications

References 57 publications

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks

An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach

Human and Animal Behavior Understanding

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