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  1. Article ; Online: Effect of vacancy defects on the electronic and mechanical properties of two-dimensional MoSi

    Dastider, Ankan Ghosh / Rasul, Ashiqur / Rahman, Ehsanur / Alam, Md Kawsar

    RSC advances

    2023  Volume 13, Issue 8, Page(s) 5307–5316

    Abstract: ... ...

    Abstract MoSi
    Language English
    Publishing date 2023-02-10
    Publishing country England
    Document type Journal Article
    ISSN 2046-2069
    ISSN (online) 2046-2069
    DOI 10.1039/d2ra07483d
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos.

    Sadik, Farhan / Dastider, Ankan Ghosh / Fattah, Shaikh Anowarul

    Health information science and systems

    2021  Volume 9, Issue 1, Page(s) 28

    Abstract: Lung Ultrasound (LUS) images are considered to be effective for detecting Coronavirus Disease (COVID-19) as an alternative to the existing reverse transcription-polymerase chain reaction (RT-PCR)-based detection scheme. However, the recent literature ... ...

    Abstract Lung Ultrasound (LUS) images are considered to be effective for detecting Coronavirus Disease (COVID-19) as an alternative to the existing reverse transcription-polymerase chain reaction (RT-PCR)-based detection scheme. However, the recent literature exhibits a shortage of works dealing with LUS image-based COVID-19 detection. In this paper, a spectral mask enhancement (SpecMEn) scheme is introduced along with a histogram equalization pre-processing stage to reduce the noise effect in LUS images prior to utilizing them for feature extraction. In order to detect the COVID-19 cases, we propose to utilize the SpecMEn pre-processed LUS images in the deep learning (DL) models (namely the SpecMEn-DL method), which offers a better representation of some characteristics features in LUS images and results in very satisfactory classification performance. The performance of the proposed SpecMEn-DL technique is appraised by implementing some state-of-the-art DL models and comparing the results with related studies. It is found that the use of the SpecMEn scheme in DL techniques offers an average increase in accuracy and
    Language English
    Publishing date 2021-07-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2697647-X
    ISSN 2047-2501
    ISSN 2047-2501
    DOI 10.1007/s13755-021-00154-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound.

    Dastider, Ankan Ghosh / Sadik, Farhan / Fattah, Shaikh Anowarul

    Computers in biology and medicine

    2021  Volume 132, Page(s) 104296

    Abstract: The COVID-19 pandemic has become one of the biggest threats to the global healthcare system, creating an unprecedented condition worldwide. The necessity of rapid diagnosis calls for alternative methods to predict the condition of the patient, for which ... ...

    Abstract The COVID-19 pandemic has become one of the biggest threats to the global healthcare system, creating an unprecedented condition worldwide. The necessity of rapid diagnosis calls for alternative methods to predict the condition of the patient, for which disease severity estimation on the basis of Lung Ultrasound (LUS) can be a safe, radiation-free, flexible, and favorable option. In this paper, a frame-based 4-score disease severity prediction architecture is proposed with the integration of deep convolutional and recurrent neural networks to consider both spatial and temporal features of the LUS frames. The proposed convolutional neural network (CNN) architecture implements an autoencoder network and separable convolutional branches fused with a modified DenseNet-201 network to build a vigorous, noise-free classification model. A five-fold cross-validation scheme is performed to affirm the efficacy of the proposed network. In-depth result analysis shows a promising improvement in the classification performance by introducing the Long Short-Term Memory (LSTM) layers after the proposed CNN architecture by an average of 7-12%, which is approximately 17% more than the traditional DenseNet architecture alone. From an extensive analysis, it is found that the proposed end-to-end scheme is very effective in detecting COVID-19 severity scores from LUS images.
    MeSH term(s) COVID-19 ; Humans ; Lung/diagnostic imaging ; Neural Networks, Computer ; Pandemics ; SARS-CoV-2
    Language English
    Publishing date 2021-02-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2021.104296
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 653: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images.

    Sadik, Farhan / Dastider, Ankan Ghosh / Subah, Mohseu Rashid / Mahmud, Tanvir / Fattah, Shaikh Anowarul

    Computers in biology and medicine

    2022  Volume 149, Page(s) 105806

    Abstract: In the Coronavirus disease-2019 (COVID-19) pandemic, for fast and accurate diagnosis of a large number of patients, besides traditional methods, automated diagnostic tools are now extremely required. In this paper, a deep convolutional neural network ( ... ...

    Abstract In the Coronavirus disease-2019 (COVID-19) pandemic, for fast and accurate diagnosis of a large number of patients, besides traditional methods, automated diagnostic tools are now extremely required. In this paper, a deep convolutional neural network (CNN) based scheme is proposed for automated accurate diagnosis of COVID-19 from lung computed tomography (CT) scan images. First, for the automated segmentation of lung regions in a chest CT scan, a modified CNN architecture, namely SKICU-Net is proposed by incorporating additional skip interconnections in the U-Net model that overcome the loss of information in dimension scaling. Next, an agglomerative hierarchical clustering is deployed to eliminate the CT slices without significant information. Finally, for effective feature extraction and diagnosis of COVID-19 and pneumonia from the segmented lung slices, a modified DenseNet architecture, namely P-DenseCOVNet is designed where parallel convolutional paths are introduced on top of the conventional DenseNet model for getting better performance through overcoming the loss of positional arguments. Outstanding performances have been achieved with an F
    MeSH term(s) COVID-19/diagnostic imaging ; COVID-19 Testing ; Humans ; Lung/diagnostic imaging ; Neural Networks, Computer ; Pandemics ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2022-07-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.105806
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 653: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Book ; Online: Topological, or Non-topological? A Deep Learning Based Prediction

    Rasul, Ashiqur / Hossain, Md Shafayat / Dastider, Ankan Ghosh / Roy, Himaddri / Hasan, M. Zahid / Khosru, Quazi D. M.

    2023  

    Abstract: Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ... ...

    Abstract Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology and graph neural network which offers an accuracy of 91.4% and an F1 score of 88.5% in classifying topological vs. non-topological materials, outperforming the other state-of-the-art classifier models. The incorporation of the graph neural network encodes the underlying relation between the atoms into the model based on their own crystalline structures and thus proved to be an effective method to represent and process non-euclidean data like molecules with a relatively shallow network. The persistent homology pipeline in the suggested neural network is capable of integrating the atom-specific topological information into the deep learning model, increasing robustness, and gain in performance. It is believed that the presented work will be an efficacious tool for predicting the topological class and therefore enable the high-throughput search for novel materials in this field.

    Comment: 13 pages, 8 figures
    Keywords Condensed Matter - Materials Science ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-10-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Privacy-Aware Activity Classification from First Person Office Videos

    Ghosh, Partho / Istiak, Md. Abrar / Rashid, Nayeeb / Akash, Ahsan Habib / Abrar, Ridwan / Dastider, Ankan Ghosh / Sushmit, Asif Shahriyar / Hasan, Taufiq

    2020  

    Abstract: In the advent of wearable body-cameras, human activity classification from First-Person Videos (FPV) has become a topic of increasing importance for various applications, including in life-logging, law-enforcement, sports, workplace, and healthcare. One ... ...

    Abstract In the advent of wearable body-cameras, human activity classification from First-Person Videos (FPV) has become a topic of increasing importance for various applications, including in life-logging, law-enforcement, sports, workplace, and healthcare. One of the challenging aspects of FPV is its exposure to potentially sensitive objects within the user's field of view. In this work, we developed a privacy-aware activity classification system focusing on office videos. We utilized a Mask-RCNN with an Inception-ResNet hybrid as a feature extractor for detecting, and then blurring out sensitive objects (e.g., digital screens, human face, paper) from the videos. For activity classification, we incorporate an ensemble of Recurrent Neural Networks (RNNs) with ResNet, ResNext, and DenseNet based feature extractors. The proposed system was trained and evaluated on the FPV office video dataset that includes 18-classes made available through the IEEE Video and Image Processing (VIP) Cup 2019 competition. On the original unprotected FPVs, the proposed activity classifier ensemble reached an accuracy of 85.078% with precision, recall, and F1 scores of 0.88, 0.85 & 0.86, respectively. On privacy protected videos, the performances were slightly degraded, with accuracy, precision, recall, and F1 scores at 73.68%, 0.79, 0.75, and 0.74, respectively. The presented system won the 3rd prize in the IEEE VIP Cup 2019 competition.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2020-06-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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