LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 7 of total 7

Search options

  1. Article ; Online: LBP-based information assisted intelligent system for COVID-19 identification.

    Maheshwari, Shishir / Sharma, Rishi Raj / Kumar, Mohit

    Computers in biology and medicine

    2021  Volume 134, Page(s) 104453

    Abstract: A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the ... ...

    Abstract A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.
    Language English
    Publishing date 2021-05-01
    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.2021.104453
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    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)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article: EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases.

    Sharma, Rishi Raj / Kumar, Mohit / Maheshwari, Shishir / Ray, Kamla Prasan

    IEEE transactions on instrumentation and measurement

    2020  Volume 70, Page(s) 6502210

    Abstract: The time-series forecasting makes a substantial contribution in timely decision-making. In this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA) is applied to ... ...

    Abstract The time-series forecasting makes a substantial contribution in timely decision-making. In this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA) is applied to develop a forecasting model for nonstationary time series. The Phillips-Perron test (PPT) is used to define the nonstationarity of time series. EVDHM is applied over a time series to decompose it into respective subcomponents and reduce the nonstationarity. ARIMA-based model is designed to forecast the future values for each subcomponent. The forecast values of each subcomponent are added to get the final output values. The optimized value of ARIMA parameters for each subcomponent is obtained using a genetic algorithm (GA) for minimum values of Akaike information criterion (AIC). Model performance is evaluated by estimating the future values of daily new cases of the recent pandemic disease COVID-19 for India, USA, and Brazil. The high efficacy of the proposed method is convinced with the results.
    Language English
    Publishing date 2020-12-02
    Publishing country United States
    Document type Journal Article
    ISSN 0018-9456
    ISSN 0018-9456
    DOI 10.1109/TIM.2020.3041833
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    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.

  3. Book ; Online: CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation

    Maheshwari, Shishir / Kanhangad, Vivek / Pachori, Ram Bilas

    2020  

    Abstract: Glaucoma causes an irreversible damage to retinal nerve fibers which results in vision loss, if undetected in early stage. Therefore, diagnosis of glaucoma in its early stage may prevent further vision loss. In this paper, we propose a convolutional ... ...

    Abstract Glaucoma causes an irreversible damage to retinal nerve fibers which results in vision loss, if undetected in early stage. Therefore, diagnosis of glaucoma in its early stage may prevent further vision loss. In this paper, we propose a convolutional neural network (CNN) based approach for automated glaucoma diagnosis by employing retinal fundus images. This approach employs transfer learning technique and local binary pattern (LBP) based data augmentation. In the proposed approach, we employ Alexnet as a pre-trained CNN model which is used for transfer learning. Initially, the proposed approach divides the fundus image dataset into training and testing data. Further, the color fundus images in training and testing data are separated into red (R), green (G), and blue (B) channels. Additionally, the LBP-based data augmentation is performed on training data. Specifically, we compute LPBs for each of the channel. Finally, the augmented training data is used to train the CNN model via transfer learning. In testing stage, the R, G, and B channels of test image are fed to the trained CNN model which generates 3 decisions. We employ a decision level fusion technique to combine the decisions obtained from the trained CNN model. The experimental evaluation of the proposed approach on the public RIM-ONE fundus image database, achieves state-of-the-art performance for glaucoma diagnosis.

    Comment: 7 pages, 3 figures, 4 tables
    Keywords Electrical Engineering and Systems Science - Signal Processing
    Subject code 006
    Publishing date 2020-02-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    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.

  4. Article ; Online: Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images.

    Maheshwari, Shishir / Pachori, Ram Bilas / Acharya, U Rajendra

    IEEE journal of biomedical and health informatics

    2017  Volume 21, Issue 3, Page(s) 803–813

    Abstract: Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and ...

    Abstract Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and optical coherence tomography. These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an automated diagnosis of glaucoma using digital fundus images based on empirical wavelet transform (EWT). The EWT is used to decompose the image, and correntropy features are obtained from decomposed EWT components. These extracted features are ranked based on t value feature selection algorithm. Then, these features are used for the classification of normal and glaucoma images using least-squares support vector machine (LS-SVM) classifier. The LS-SVM is employed for classification with radial basis function, Morlet wavelet, and Mexican-hat wavelet kernels. The classification accuracy of the proposed method is 98.33% and 96.67% using threefold and tenfold cross validation, respectively.
    Language English
    Publishing date 2017-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2016.2544961
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 5483: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  5. Article ; Online: Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques.

    Maheshwari, Shishir / Kanhangad, Vivek / Pachori, Ram Bilas / Bhandary, Sulatha V / Acharya, U Rajendra

    Computers in biology and medicine

    2018  Volume 105, Page(s) 72–80

    Abstract: Background and objective: Glaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinical methods and imaging ... ...

    Abstract Background and objective: Glaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinical methods and imaging techniques are manual and require skilled supervision. For the purpose of mass screening, an automated system is needed for glaucoma diagnosis which is fast, accurate, and helps in reducing the burden on experts.
    Methods: In this work, we present a bit-plane slicing (BPS) and local binary pattern (LBP) based novel approach for glaucoma diagnosis. Firstly, our approach separates the red (R), green (G), and blue (B) channels from the input color fundus image and splits the channels into bit planes. Secondly, we extract LBP based statistical features from each of the bit planes of the individual channels. Thirdly, these features from the individual channels are fed separately to three different support vector machines (SVMs) for classification. Finally, the decisions from the individual SVMs are fused at the decision level to classify the input fundus image into normal or glaucoma class.
    Results: Our experimental results suggest that the proposed approach is effective in discriminating normal and glaucoma cases with an accuracy of 99.30% using 10-fold cross validation.
    Conclusions: The developed system is ready to be tested on large and diverse databases and can assist the ophthalmologists in their daily screening to confirm their diagnosis, thereby increasing accuracy of diagnosis.
    MeSH term(s) Fundus Oculi ; Glaucoma/diagnostic imaging ; Humans ; Image Interpretation, Computer-Assisted ; Support Vector Machine
    Language English
    Publishing date 2018-12-05
    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.2018.11.028
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    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)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Article ; Online: Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

    Maheshwari, Shishir / Pachori, Ram Bilas / Kanhangad, Vivek / Bhandary, Sulatha V / Acharya, U Rajendra

    Computers in biology and medicine

    2017  Volume 88, Page(s) 142–149

    Abstract: Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time ... ...

    Abstract Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.
    MeSH term(s) Algorithms ; Diagnostic Techniques, Ophthalmological ; Entropy ; Fundus Oculi ; Glaucoma/diagnosis ; Humans ; Image Interpretation, Computer-Assisted/methods ; Least-Squares Analysis ; Retina/diagnostic imaging
    Language English
    Publishing date 2017--01
    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.2017.06.017
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    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)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  7. Article ; Online: IMPETUS Stroke: Assessment of hospital infrastructure and workflow for implementation of uniform stroke care pathway in India.

    Salunkhe, Manish / Haldar, Partha / Bhatia, Rohit / Prasad, Deepshikha / Gupta, Shweta / Srivastava, M V Padma / Bhoi, Sanjeev / Jha, Menka / Samal, Priyanka / Panda, Samhita / Anand, Sucharita / Kumar, Niraj / Tiwari, Ashutosh / Gopi, S / Raju, Garuda Butchi / Garg, Jyoti / Chawla, M P S / Ray, Biman Kanti / Bhardwaj, Amit /
    Verma, Alok / Dongre, Nikhil / Chhina, Gurpreet / Sibia, Raminder / Kaur, Rupinderjeet / Zanzmera, Paresh / Iype, Thomas / Sulena / Garg, Ravinder / Kumar, Ashok / Ranjan, Abhay / Sardana, Vijay / Maheshwari, Dilip / Bhushan, Bharat / Saluja, Alvee / Darole, Pramod / Bala, Kiran / Dabla, Surekha / Puri, Inder / Shah, Shalin / Ranga, Gajender Singh / Nath, Smita / Chandan, Shishir / Malik, Rupali

    International journal of stroke : official journal of the International Stroke Society

    2023  Volume 19, Issue 1, Page(s) 76–83

    Abstract: Background: India accounts for 13.3% of global disability-adjusted life years (DALYs) lost due to stroke with a relatively younger age of onset compared to the Western population. In India's public healthcare system, many stroke patients seek care at ... ...

    Abstract Background: India accounts for 13.3% of global disability-adjusted life years (DALYs) lost due to stroke with a relatively younger age of onset compared to the Western population. In India's public healthcare system, many stroke patients seek care at tertiary-level government-funded medical colleges where an optimal level of stroke care is expected. However, there are no studies from India that have assessed the quality of stroke care, including infrastructure, imaging facilities, or the availability of stroke care units in medical colleges.
    Aim: This study aimed to understand the existing protocols and management of acute stroke care across 22 medical colleges in India, as part of the baseline assessment of the ongoing IMPETUS stroke study.
    Methods: A semi-structured quantitative pre-tested questionnaire, developed based on review of literature and expert discussion, was mailed to 22 participating sites of the IMPETUS stroke study. The questionnaire assessed comprehensively all components of stroke care, including human resources, emergency system, in-hospital care, and secondary prevention. A descriptive analysis of their status was undertaken.
    Results: In the emergency services, limited stroke helpline numbers, 3/22 (14%); prenotification system, 5/22 (23%); and stroke-trained physicians were available, 6/22 (27%). One-third of hospitals did not have on-call neurologists. Although non-contrast computed tomography (NCCT) was always available, 39% of hospitals were not doing computed tomography (CT) angiography and 13/22 (59%) were not doing magnetic resonance imaging (MRI) after routine working hours. Intravenous thrombolysis was being done in 20/22 (91%) hospitals, but 36% of hospitals did not provide it free of cost. Endovascular therapy was available only in 6/22 (27%) hospitals. The study highlighted the scarcity of multidisciplinary stroke teams, 8/22 (36%), and stroke units, 7/22 (32%). Lifesaving surgeries like hematoma evacuation, 11/22 (50%), and decompressive craniectomy, 9/22 (41%), were performed in limited numbers. The availability of occupational therapists, speech therapists, and cognitive rehabilitation was minimal.
    Conclusion: This study highlighted the current status of acute stroke management in publicly funded tertiary care hospitals. Lack of prenotification, limited number of stroke-trained physicians and neurosurgeons, relatively lesser provision of free thrombolytic agents, limited stroke units, and lack of rehabilitation services are areas needing urgent attention by policymakers and creation of sustainable education models for uniform stroke care by medical professionals across the country.
    MeSH term(s) Humans ; Stroke/epidemiology ; Stroke/therapy ; Workflow ; Critical Pathways ; Hospitals ; Delivery of Health Care
    Language English
    Publishing date 2023-08-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2303728-3
    ISSN 1747-4949 ; 1747-4930
    ISSN (online) 1747-4949
    ISSN 1747-4930
    DOI 10.1177/17474930231189395
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 6582: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top