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  1. Article ; Online: Deep learning techniques for automated detection of autism spectrum disorder based on thermal imaging.

    Ganesh, Kavya / Umapathy, Snekhalatha / Thanaraj Krishnan, Palani

    Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine

    2021  Volume 235, Issue 10, Page(s) 1113–1127

    Abstract: Children with autism spectrum disorder have impairments in emotional processing which leads to the inability in recognizing facial expressions. Since emotion is a vital criterion for having fine socialisation, it is incredibly important for the autistic ... ...

    Abstract Children with autism spectrum disorder have impairments in emotional processing which leads to the inability in recognizing facial expressions. Since emotion is a vital criterion for having fine socialisation, it is incredibly important for the autistic children to recognise emotions. In our study, we have chosen the facial skin temperature as a biomarker to measure emotions. To assess the facial skin temperature, the thermal imaging modality has been used in this study, since it has been recognised as a promising technique to evaluate emotional responses. The aim of this study was the following: (1) to compare the facial skin temperature of autistic and non-autistic children by using thermal imaging across various emotions; (2) to classify the thermal images obtained from the study using the customised convolutional neural network compared with the ResNet 50 network. Fifty autistic and fifty non-autistic participants were included for the study. Thermal imaging was used to obtain the temperature of specific facial regions such as the eyes, cheek, forehead and nose while we evoked emotions (Happiness, anger and sadness) in children using an audio-visual stimulus. Among the emotions considered, the emotion anger had the highest temperature difference between the autistic and non-autistic participants in the region's eyes (1.9%), cheek (2.38%) and nose (12.6%). The accuracy obtained by classifying the thermal images of the autistic and non-autistic children using Customised Neural Network and ResNet 50 Network was 96% and 90% respectively. This computer aided diagnostic tool can be a predictable and a steadfast method in the diagnosis of the autistic individuals.
    MeSH term(s) Autism Spectrum Disorder/diagnostic imaging ; Autistic Disorder ; Child ; Deep Learning ; Emotions ; Facial Expression ; Humans
    Language English
    Publishing date 2021-06-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 1065942-0
    ISSN 2041-3033 ; 0046-2039 ; 0954-4119
    ISSN (online) 2041-3033
    ISSN 0046-2039 ; 0954-4119
    DOI 10.1177/09544119211024778
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Firefly-Algorithm Supported Scheme to Detect COVID-19 Lesion in Lung CT Scan Images using Shannon Entropy and Markov-Random-Field

    Rajinikanth, Venkatesan / Kadry, Seifedine / Thanaraj, Krishnan Palani / Kamalanand, Krishnamurthy / Seo, Sanghyun

    Abstract: The pneumonia caused by Coronavirus disease (COVID-19) is one of major global threat and a number of detection and treatment procedures are suggested by the researchers for COVID-19. The proposed work aims to suggest an automated image processing scheme ... ...

    Abstract The pneumonia caused by Coronavirus disease (COVID-19) is one of major global threat and a number of detection and treatment procedures are suggested by the researchers for COVID-19. The proposed work aims to suggest an automated image processing scheme to extract the COVID-19 lesion from the lung CT scan images (CTI) recorded from the patients. This scheme implements the following procedures; (i) Image pre-processing to enhance the COVID-19 lesions, (ii) Image post-processing to extract the lesions, and (iii) Execution of a relative analysis between the extracted lesion segment and the Ground-Truth-Image (GTI). This work implements Firefly Algorithm and Shannon Entropy (FA+SE) based multi-threshold to enhance the pneumonia lesion and implements Markov-Random-Field (MRF) segmentation to extract the lesions with better accuracy. The proposed scheme is tested and validated using a class of COVID-19 CTI obtained from the existing image datasets and the experimental outcome is appraised to authenticate the clinical significance of the proposed scheme. The proposed work helped to attain a mean accuracy of>92% during COVID-19 lesion segmentation and in future, it can be used to examine the real clinical lung CTI of COVID-19 patients.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  3. Article: Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class

    Kadry, Seifedine / Rajinikanth, Venkatesan / Rho, Seungmin / Raja, Nadaradjane Sri Madhava / Rao, Vaddi Seshagiri / Thanaraj, Krishnan Palani

    Abstract: Recently, the lung infection due to Coronavirus Disease (COVID-19) affected a large human group worldwide and the assessment of the infection rate in the lung is essential for treatment planning. This research aims to propose a Machine-Learning-System ( ... ...

    Abstract Recently, the lung infection due to Coronavirus Disease (COVID-19) affected a large human group worldwide and the assessment of the infection rate in the lung is essential for treatment planning. This research aims to propose a Machine-Learning-System (MLS) to detect the COVID-19 infection using the CT scan Slices (CTS). This MLS implements a sequence of methods, such as multi-thresholding, image separation using threshold filter, feature-extraction, feature-selection, feature-fusion and classification. The initial part implements the Chaotic-Bat-Algorithm and Kapur's Entropy (CBA+KE) thresholding to enhance the CTS. The threshold filter separates the image into two segments based on a chosen threshold 'Th'. The texture features of these images are extracted, refined and selected using the chosen procedures. Finally, a two-class classifier system is implemented to categorize the chosen CTS (n=500 with a pixel dimension of 512x512x1) into normal/COVID-19 group. In this work, the classifiers, such as Naive Bayes (NB), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Support Vector Machine with linear kernel (SVM) are implemented and the classification task is performed using various feature vectors. The experimental outcome of the SVM with Fused-Feature-Vector (FFV) helped to attain a detection accuracy of 89.80%.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  4. Book ; Online: Firefly-Algorithm Supported Scheme to Detect COVID-19 Lesion in Lung CT Scan Images using Shannon Entropy and Markov-Random-Field

    Rajinikanth, Venkatesan / Kadry, Seifedine / Thanaraj, Krishnan Palani / Kamalanand, Krishnamurthy / Seo, Sanghyun

    2020  

    Abstract: The pneumonia caused by Coronavirus disease (COVID-19) is one of major global threat and a number of detection and treatment procedures are suggested by the researchers for COVID-19. The proposed work aims to suggest an automated image processing scheme ... ...

    Abstract The pneumonia caused by Coronavirus disease (COVID-19) is one of major global threat and a number of detection and treatment procedures are suggested by the researchers for COVID-19. The proposed work aims to suggest an automated image processing scheme to extract the COVID-19 lesion from the lung CT scan images (CTI) recorded from the patients. This scheme implements the following procedures; (i) Image pre-processing to enhance the COVID-19 lesions, (ii) Image post-processing to extract the lesions, and (iii) Execution of a relative analysis between the extracted lesion segment and the Ground-Truth-Image (GTI). This work implements Firefly Algorithm and Shannon Entropy (FA+SE) based multi-threshold to enhance the pneumonia lesion and implements Markov-Random-Field (MRF) segmentation to extract the lesions with better accuracy. The proposed scheme is tested and validated using a class of COVID-19 CTI obtained from the existing image datasets and the experimental outcome is appraised to authenticate the clinical significance of the proposed scheme. The proposed work helped to attain a mean accuracy of >92% during COVID-19 lesion segmentation and in future, it can be used to examine the real clinical lung CTI of COVID-19 patients.

    Comment: 12 pages
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; covid19
    Subject code 006
    Publishing date 2020-04-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Book ; Online: Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class

    Kadry, Seifedine / Rajinikanth, Venkatesan / Rho, Seungmin / Raja, Nadaradjane Sri Madhava / Rao, Vaddi Seshagiri / Thanaraj, Krishnan Palani

    2020  

    Abstract: Recently, the lung infection due to Coronavirus Disease (COVID-19) affected a large human group worldwide and the assessment of the infection rate in the lung is essential for treatment planning. This research aims to propose a Machine-Learning-System ( ... ...

    Abstract Recently, the lung infection due to Coronavirus Disease (COVID-19) affected a large human group worldwide and the assessment of the infection rate in the lung is essential for treatment planning. This research aims to propose a Machine-Learning-System (MLS) to detect the COVID-19 infection using the CT scan Slices (CTS). This MLS implements a sequence of methods, such as multi-thresholding, image separation using threshold filter, feature-extraction, feature-selection, feature-fusion and classification. The initial part implements the Chaotic-Bat-Algorithm and Kapur's Entropy (CBA+KE) thresholding to enhance the CTS. The threshold filter separates the image into two segments based on a chosen threshold 'Th'. The texture features of these images are extracted, refined and selected using the chosen procedures. Finally, a two-class classifier system is implemented to categorize the chosen CTS (n=500 with a pixel dimension of 512x512x1) into normal/COVID-19 group. In this work, the classifiers, such as Naive Bayes (NB), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Support Vector Machine with linear kernel (SVM) are implemented and the classification task is performed using various feature vectors. The experimental outcome of the SVM with Fused-Feature-Vector (FFV) helped to attain a detection accuracy of 89.80%.

    Comment: 16 PAGES
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Machine Learning ; Statistics - Machine Learning ; covid19
    Subject code 006
    Publishing date 2020-04-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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