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  1. Article ; Online: Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.

    Sitaula, Chiranjibi / Shahi, Tej Bahadur

    Journal of medical systems

    2022  Volume 46, Issue 11, Page(s) 78

    Abstract: Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread ... ...

    Abstract Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.
    MeSH term(s) COVID-19/diagnosis ; Deep Learning ; Humans ; Monkeypox virus ; Pandemics
    Language English
    Publishing date 2022-10-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-022-01868-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia.

    Aryal, Jagannath / Sitaula, Chiranjibi / Frery, Alejandro C

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 13510

    Abstract: Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been ...

    Abstract Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been made considering spectral (Sp), statistical (St), and index-based (Ind) features in developing LULC maps for city planning. However, no work has been reported to automate LULC performance modeling for their robustness with machine learning (ML) algorithms. In this paper, we design seven schemes and automate the LULC performance modeling with six ML algorithms-Random Forest, Support Vector Machine with Linear kernel, Support Vector Machine with Radial basis function kernel, Artificial Neural Network, Naïve Bayes, and Generalised Linear Model for the city of Melbourne, Australia on Sentinel-2A images. Experimental results show that the Random Forest outperforms remaining ML algorithms in the classification accuracy (0.99) on all schemes. The robustness and statistical analysis of the ML algorithms (for example, Random Forest imparts over 0.99 F1-score for all five categories and p value [Formula: see text] 0.05 from Wilcoxon ranked test over accuracy measures) against varying training splits demonstrate the effectiveness of the proposed schemes. Thus, providing a robust measure of LULC maps in city planning.
    Language English
    Publishing date 2023-08-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-40564-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis.

    Sitaula, Chiranjibi / Aryal, Sunil

    Health information science and systems

    2021  Volume 9, Issue 1, Page(s) 24

    Abstract: Purpose: Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the Pneumonia-like effect in the lung, the examination of Chest X-Rays (CXR) can help diagnose the disease. For automatic analysis of images, they are ... ...

    Abstract Purpose: Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the Pneumonia-like effect in the lung, the examination of Chest X-Rays (CXR) can help diagnose the disease. For automatic analysis of images, they are represented in machines by a set of semantic features. Deep Learning (DL) models are widely used to extract features from images. General deep features extracted from intermediate layers may not be appropriate to represent CXR images as they have a few semantic regions. Though the Bag of Visual Words (BoVW)-based features are shown to be more appropriate for different types of images, existing BoVW features may not capture enough information to differentiate COVID-19 infection from other Pneumonia-related infections.
    Methods: In this paper, we propose a new BoVW method over deep features, called Bag of Deep Visual Words (BoDVW), by removing the feature map normalization step and adding the deep features normalization step on the raw feature maps. This helps to preserve the semantics of each feature map that may have important clues to differentiate COVID-19 from Pneumonia.
    Results: We evaluate the effectiveness of our proposed BoDVW features in CXR image classification using Support Vector Machine (SVM) to diagnose COVID-19. Our results on four publicly available COVID-19 CXR image datasets (D1, D2, D3, and D4) reveal that our features produce stable and prominent classification accuracy (82.00% on D1, 87.86% on D2, 87.92% on D3, and 83.22% on D4), particularly differentiating COVID-19 infection from other Pneumonia.
    Conclusion: Our method could be a very useful tool for the quick diagnosis of COVID-19 patients on a large scale.
    Language English
    Publishing date 2021-06-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-021-00152-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Publisher Correction: Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection.

    Sitaula, Chiranjibi / Shahi, Tej Bahadur / Aryal, Sunil / Marzbanrad, Faezeh

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 1010

    Language English
    Publishing date 2022-01-13
    Publishing country England
    Document type Published Erratum
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-05240-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: NDVI Threshold-Based Urban Green Space Mapping from Sentinel-2A at the Local Governmental Area (LGA) Level of Victoria, Australia

    Jagannath Aryal / Chiranjibi Sitaula / Sunil Aryal

    Land, Vol 11, Iss 351, p

    2022  Volume 351

    Abstract: Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning ... ...

    Abstract Obtaining accurate, precise and timely spatial information on the distribution and dynamics of urban green space is crucial in understanding livability of the cities and urban dwellers. Inspired from the importance of spatial information in planning urban lives, and availability of state-of-the-art remote sensing data and technologies in open access forms, in this work, we develop a simple three-level hierarchical mapping of urban green space with multiple usability to various stakeholders. We utilize the established Normalized Difference Vegetation Index (NDVI) threshold on Sentinel-2A Earth Observation image data to classify the urban vegetation of each Victorian Local Government Area (LGA). Firstly, we categorize each LGA region into two broad classes as vegetation and non-vegetation; secondly, we further categorize the vegetation regions of each LGA into two sub-classes as shrub (including grassland) and trees; thirdly, for both shrub and trees classes, we further classify them as stressed and healthy. We not only map the urban vegetation in hierarchy but also develop Urban Green Space Index ( UGSI ) and Per Capita Green Space ( PCGS ) for the Victorian Local Government Areas (LGAs) to provide insights on the association of demography with urban green infrastructure using urban spatial analytics. To show the efficacy of the applied method, we evaluate our results using a Google Earth Engine (GEE) platform across different NDVI threshold ranges. The evaluation result shows that our method produces excellent performance metrics such as mean precision, recall, f-score and accuracy. In addition to this, we also prepare a recent Sentinel-2A dataset and derived products of urban green space coverage of the Victorian LGAs that are useful for multiple stakeholders ranging from bushfire modellers to biodiversity conservationists in contributing to sustainable and resilient urban lives.
    Keywords land cover classification ; LGA ; NDVI ; Sentinel-2A ; spatial information ; sustainability ; Agriculture ; S
    Subject code 710
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Fusion of whole and part features for the classification of histopathological image of breast tissue.

    Sitaula, Chiranjibi / Aryal, Sunil

    Health information science and systems

    2020  Volume 8, Issue 1, Page(s) 38

    Abstract: Purpose: Nowadays Computer-Aided Diagnosis (CAD) models, particularly those based on deep learning, have been widely used to analyze histopathological images in breast cancer diagnosis. However, due to the limited availability of such images, it is ... ...

    Abstract Purpose: Nowadays Computer-Aided Diagnosis (CAD) models, particularly those based on deep learning, have been widely used to analyze histopathological images in breast cancer diagnosis. However, due to the limited availability of such images, it is always tedious to train deep learning models that require a huge amount of training data. In this paper, we propose a new deep learning-based CAD framework that can work with less amount of training data.
    Methods: We use pre-trained models to extract image features that can then be used with any classifier. Our proposed features are extracted by the fusion of two different types of features (foreground and background) at two levels (whole-level and part-level). Foreground and background feature to capture information about different structures and their layout in microscopic images of breast tissues. Similarly, part-level and whole-level features capture are useful in detecting interesting regions scattered in high-resolution histopathological images at local and whole image levels. At each level, we use VGG16 models pre-trained on ImageNet and Places datasets to extract foreground and background features, respectively. All features are extracted from mid-level pooling layers of such models.
    Results: We show that proposed fused features with a Support Vector Classifier (SVM) produce better classification accuracy than recent methods on BACH dataset and our approach is orders of magnitude faster than the best performing recent method (EMS-Net).
    Conclusion: We believe that our method would be another alternative in the diagnosis of breast cancer because of performance and prediction time.
    Language English
    Publishing date 2020-11-04
    Publishing country England
    Document type Journal Article
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-020-00131-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Attention-based VGG-16 model for COVID-19 chest X-ray image classification.

    Sitaula, Chiranjibi / Hossain, Mohammad Belayet

    Applied intelligence (Dordrecht, Netherlands)

    2020  Volume 51, Issue 5, Page(s) 2850–2863

    Abstract: Computer-aided diagnosis (CAD) methods such as Chest X-rays (CXR)-based method is one of the cheapest alternative options to diagnose the early stage of COVID-19 disease compared to other alternatives such as Polymerase Chain Reaction (PCR), Computed ... ...

    Abstract Computer-aided diagnosis (CAD) methods such as Chest X-rays (CXR)-based method is one of the cheapest alternative options to diagnose the early stage of COVID-19 disease compared to other alternatives such as Polymerase Chain Reaction (PCR), Computed Tomography (CT) scan, and so on. To this end, there have been few works proposed to diagnose COVID-19 by using CXR-based methods. However, they have limited performance as they ignore the spatial relationships between the region of interests (ROIs) in CXR images, which could identify the likely regions of COVID-19's effect in the human lungs. In this paper, we propose a novel attention-based deep learning model using the attention module with VGG-16. By using the attention module, we capture the spatial relationship between the ROIs in CXR images. In the meantime, by using an appropriate convolution layer (4th pooling layer) of the VGG-16 model in addition to the attention module, we design a novel deep learning model to perform fine-tuning in the classification process. To evaluate the performance of our method, we conduct extensive experiments by using three COVID-19 CXR image datasets. The experiment and analysis demonstrate the stable and promising performance of our proposed method compared to the state-of-the-art methods. The promising classification performance of our proposed method indicates that it is suitable for CXR image classification in COVID-19 diagnosis.
    Language English
    Publishing date 2020-11-17
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1479519-X
    ISSN 1573-7497 ; 0924-669X
    ISSN (online) 1573-7497
    ISSN 0924-669X
    DOI 10.1007/s10489-020-02055-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Publisher Correction

    Chiranjibi Sitaula / Tej Bahadur Shahi / Sunil Aryal / Faezeh Marzbanrad

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection

    2022  Volume 1

    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification

    Sitaula, Chiranjibi / KC, Sumesh / Aryal, Jagannath

    2023  

    Abstract: Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems. Recently, the existing deep learning (DL)-based methods have shown great promise in ... ...

    Abstract Very high-resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class variability problems. Recently, the existing deep learning (DL)-based methods have shown great promise in VHR RS scene classification. However, they still provide an unstable classification performance. To address such a problem, we, in this letter, propose a novel DL-based approach. For this, we devise an enhanced VHR attention module (EAM), followed by the atrous spatial pyramid pooling (ASPP) and global average pooling (GAP). This procedure imparts the enhanced features from the corresponding level. Then, the multi-level feature fusion is performed. Experimental results on two widely-used VHR RS datasets show that the proposed approach yields a competitive and stable/robust classification performance with the least standard deviation of 0.001. Further, the highest overall accuracies on the AID and the NWPU datasets are 95.39% and 93.04%, respectively.

    Comment: This paper is under consideration in the International Journal of Intelligent Systems (Wiley) journal. Based on the journal's policy and restrictions, this version may be updated or deleted
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-05-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring: Part 1 wearable technology.

    Grooby, Ethan / Sitaula, Chiranjibi / Chang Kwok, T'ng / Sharkey, Don / Marzbanrad, Faezeh / Malhotra, Atul

    Pediatric research

    2023  Volume 93, Issue 2, Page(s) 413–425

    Abstract: With the development of Artificial Intelligence techniques, smart health monitoring is becoming more popular. In this study, we investigate the trend of wearable sensors being adopted and developed in neonatal cardiorespiratory monitoring. We performed a ...

    Abstract With the development of Artificial Intelligence techniques, smart health monitoring is becoming more popular. In this study, we investigate the trend of wearable sensors being adopted and developed in neonatal cardiorespiratory monitoring. We performed a search of papers published from the year 2000 onwards. We then reviewed the advances in sensor technologies and wearable modalities for this application. Common wearable modalities included clothing (39%); chest/abdominal belts (25%); and adhesive patches (15%). Popular singular physiological information from sensors included electrocardiogram (15%), breathing (24%), oxygen saturation and photoplethysmography (13%). Many studies (46%) incorporated a combination of these signals. There has been extensive research in neonatal cardiorespiratory monitoring using both single and multi-parameter systems. Poor data quality is a common issue and further research into combining multi-sensor information to alleviate this should be investigated. IMPACT STATEMENT: State-of-the-art review of sensor technology for wearable neonatal cardiorespiratory monitoring. Review of the designs for wearable neonatal cardiorespiratory monitoring. The use of multi-sensor information to improve physiological data quality has been limited in past research. Several sensor technologies have been implemented and tested on adults that have yet to be explored in the newborn population.
    MeSH term(s) Adult ; Infant, Newborn ; Humans ; Artificial Intelligence ; Monitoring, Physiologic/methods ; Wearable Electronic Devices ; Respiration
    Language English
    Publishing date 2023-01-02
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 4411-8
    ISSN 1530-0447 ; 0031-3998
    ISSN (online) 1530-0447
    ISSN 0031-3998
    DOI 10.1038/s41390-022-02416-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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