E-ISSN 0976-2833 | ISSN 0975-3583
 

Research Article 


Mitral stenosis detection using Deep Learning technique in PSAX view TTE

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh.

Abstract
The goal of this research is to identify abnormality automatically in the mitral valve,stenosis, or
normal in parasternal short-axis view (PSAX).one of the common and widely spread valvular diseases is mitral
valve disease. It is still a burden in underdeveloped countries, for health sociality as well as countries. Mitral
valve approximately 80 percent of valvular diseases. According tothe World Heart Foundation Guidelines,
based on mitral leaflets morphology. The mitral valve areacan calculate using the PSAX view. In mitral stenosis
mitral valve has a specific shape, which is similar to a fish mouth. Our main goal is to detect this abnormality so
that sonographers investigate further better.Our evaluation metrics have concern f1 score for normal is 99%, and
mitral stenosis is 99%, and accuracy is 99 percent. The dataset we for training is 900 and testing 600 for testing
purposes: confusion matrix, ROC curve, PR curve measured for our evaluation result. We created the
MobileNet inspired model to solve the classification of normal or mitral stenosis valve.Our proposed model
only detectsan abnormality in the mitral valve in the PSAX view. It reduces the time of Echocardiography.We
aim to use a minimum number of parameters to solve this problem to make real-time analysis possible

Key words: CNN, deep learning, Echocardiography, mitral stenosis, PSAX, MobileNet


 
ARTICLE TOOLS
Abstract
PDF Fulltext
How to cite this articleHow to cite this article
Citation Tools
Related Records
 Articles by Vishal Chandra
Articles by PrattayGuha Sarkar
Articles by Vinay Singh
on Google
on Google Scholar


How to Cite this Article
Pubmed Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh. Mitral stenosis detection using Deep Learning technique in PSAX view TTE. J Cardiovasc. Dis. Res.. 2021; 12(4): 658-664. doi: 10.31838/jcdr.2021.12.04.74


Web Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh. Mitral stenosis detection using Deep Learning technique in PSAX view TTE. http://www.jcdronline.org/?mno=98838 [Access: July 26, 2021]. doi: 10.31838/jcdr.2021.12.04.74


AMA (American Medical Association) Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh. Mitral stenosis detection using Deep Learning technique in PSAX view TTE. J Cardiovasc. Dis. Res.. 2021; 12(4): 658-664. doi: 10.31838/jcdr.2021.12.04.74



Vancouver/ICMJE Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh. Mitral stenosis detection using Deep Learning technique in PSAX view TTE. J Cardiovasc. Dis. Res.. (2021), [cited July 26, 2021]; 12(4): 658-664. doi: 10.31838/jcdr.2021.12.04.74



Harvard Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh (2021) Mitral stenosis detection using Deep Learning technique in PSAX view TTE. J Cardiovasc. Dis. Res., 12 (4), 658-664. doi: 10.31838/jcdr.2021.12.04.74



Turabian Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh. 2021. Mitral stenosis detection using Deep Learning technique in PSAX view TTE. Journal of Cardiovascular Disease Research, 12 (4), 658-664. doi: 10.31838/jcdr.2021.12.04.74



Chicago Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh. "Mitral stenosis detection using Deep Learning technique in PSAX view TTE." Journal of Cardiovascular Disease Research 12 (2021), 658-664. doi: 10.31838/jcdr.2021.12.04.74



MLA (The Modern Language Association) Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh. "Mitral stenosis detection using Deep Learning technique in PSAX view TTE." Journal of Cardiovascular Disease Research 12.4 (2021), 658-664. Print. doi: 10.31838/jcdr.2021.12.04.74



APA (American Psychological Association) Style

Vishal Chandra, PrattayGuha Sarkar, Vinay Singh (2021) Mitral stenosis detection using Deep Learning technique in PSAX view TTE. Journal of Cardiovascular Disease Research, 12 (4), 658-664. doi: 10.31838/jcdr.2021.12.04.74





Most Viewed Articles
  • Massive pericardial effusion as the only manifestation of primary hypothyroidism
    Radheshyam Purkait , Anand Prasad , Ramchandra Bhadra , Arindam Basu
    J Cardiovasc. Dis. Res.. 2013; 4(4): 248-250
    » Abstract » doi: 10.1016/j.jcdr.2014.01.001

  • Impact of light exercises in selective cognitive response andhandballshooting accuracy performance in Mesopotamia handball players
    Ahuda Naji Zaidan, Qusay Mohammed Hamdan, Mohammed Kadhim Saleh, Samer Saadoun Abd El , Rida
    J Cardiovasc. Dis. Res.. 2021; 12(2): 141-145
    » Abstract » doi: 10.31838/jcdr.2021.12.02.18

  • Reduced nitrate level in individuals with hypertension and diabetes
    Shiekh Gazalla Ayub, Taha Ayub, Saquib Naveed Khan, Rubiya Dar, Khurshid Iqbal Andrabi
    J Cardiovasc. Dis. Res.. 2011; 2(3): 172-176
    » Abstract » doi: 10.4103/0975-3583.85264

  • Factor analysis of risk variables associated with metabolic syndrome in adult Asian Indians
    Mithun Das, Susil Pal, Arnab Ghosh
    J Cardiovasc. Dis. Res.. 2010; 1(2): 86-91
    » Abstract » doi: 10.4103/0975-3583.64442

  • Typical coronary artery aneurysm exactly within drug-eluting stent implantation region in a patient with rheumatoid arthritis
    Ying Zheng, Jing-yuan Mao
    J Cardiovasc. Dis. Res.. 2012; 3(4): 329-331
    » Abstract » doi: 10.4103/0975-3583.102725

  • Most Downloaded
  • Assessment of the Knowledge and Attitude of Male Students towards Smoking Based on Health Belief Model
    Rafat Rezapour-Nasrabad, Fatemeh Izadi, Atousa Karimi, Fateme Shariati Far, Khatereh Rostami, Amin Kiani, Afsaneh Ghasemi
    J Cardiovasc. Dis. Res.. 2020; 11(4): 116-121
    » Abstract » doi: 10.31838/jcdr.2020.11.04.20

  • Diabetic Retinopathy, The Automated of Detection of Retinal Fundus Images with Probabilistic Neural Networks (PNN)
    Elvina Amanda, Marischa Elveny, Rahmad Syah
    J Cardiovasc. Dis. Res.. 2020; 11(4): 302-306
    » Abstract » doi: 10.31838/jcdr.2020.11.04.54

  • Investigation of the Relationship between Social Support and Adherence to Treatment among Elderly Individuals with Type II Diabetes Mellitus
    Afsaneh Ghasemi, Rafat Rezapour-Nasrabad, Leila Nikrouz, Fatemeh Izadi, Atousa Karimi, Fateme Shariati Far, Zahra Khiali
    J Cardiovasc. Dis. Res.. 2020; 11(4): 122-129
    » Abstract » doi: 10.31838/jcdr.2020.11.04.21

  • Cardio-Vascular Disease Classification Using Stacked Segmentation Model and Convolutional Neural Networks
    G. Charlyn Pushpa Latha, S. Sridhar, S. Prithi, T. Anitha
    J Cardiovasc. Dis. Res.. 2020; 11(4): 26-31
    » Abstract » doi: 10.31838/jcdr.2020.11.04.05

  • The Prediction of the Bisoprolol Effectiveness in Patients with Stable Coronary Artery Disease with Post-Infarction Cardiosclerosis
    Svetlana S. Bunova, Ol'ga V. Zamahina, Nikolaj A. Nikolaev, Nina I.Zhernakova, Andrey A.Grishchenko
    J Cardiovasc. Dis. Res.. 2020; 11(4): 105-109
    » Abstract » doi: 10.31838/jcdr.2020.11.04.18

  • Most Cited Articles