Evolution of Lung CT Image Dataset and Detection of Disease

Bhattacharjee, Shiladitya; Rahim, Lukman Ab.; Ramadhani, Ade Wahyu; Midhunchakkravarthy,; Midhunchakkravarthy, Divya

doi:10.1007/978-981-15-3284-9_48

Cited by 2 publications

(1 citation statement)

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“…Themajorriskaspectsconnectedwithmyocardialinfarctionarephysicalinactivity,unhealthy diets,highbloodpressure,obesity,highcholesterolanddiabetes (Sridharet al2021).Myocardial infarctionmainlyhappensduetothelastingdamagetotheheartmusclecausedbyspeedobstaclesin coronaryarteriesthroughshortageofbloodsupply,whichleadstoacuteinfarctionandunexpected deaths.Furthermore,thisenlargesendemicandincessantlydestroysheartmusclesifnottreatedearly. Therefore,earlyandprecisemyocardialinfarctionpredictionandclassification (Shylaja,et al,2020) enhancesthediseasedetectionandclassificationlevelanddecreasesthedeathrate (Halderet al 2019). ThedevelopmentofdigitizationenablestoacquisitionandmethodECGdataindigitalstructure (ManikandanandDandapat2007;Swainet al2020).CADsystem (Ram,et al,2019) (Shylajaand Anandan,2019;Ram,2018)commonlyusesECGsignaltoidentifycardiacdisordersbecausethese modelsarerobust,speedy,precise,andreliablecomparedwithtraditionalapproaches (Sharmaet al 2018).Besides,ECG(HaribaabuandArun,2020)isthemostappropriatemodalityforthepremature classificationofmyocardialinfarctionbecauseitisasimpleandnon-invasiveassessmentofcardiac conditions (Swainet al2020).…”

Section: Design and Development Of Hybrid Optimization-enabled Deep L...mentioning

confidence: 99%

Design and Development of Hybrid Optimization-Enabled Deep Learning Model for Myocardial Infarction

Bulbule¹,

Soma²

2022

International Journal of Sociotechnology and Knowledge Development

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Myocardial infarction is the most hazardous cardiovascular disease for humans; generally, it is acknowledged as a heart attack, which may result in death. Thus, rapid and precise detection of myocardial infarction is essential to reduce the mortality rate. This paper proposes the Taylor-enhanced invasive weed sine cosine optimization algorithm-based deep convolutional neural network (Taylor IIWSCOA-enabled DCNN) model to classify myocardial infarction. Here, the DCNN classifier is used to predict and categorize myocardial infarction, and the classifier is tuned by the Taylor IIWSCOA to attain superior efficiency. The Taylor IIWSCOA is designed by integrating SCA, IIWO approach, and the Taylor series. The proposed Taylor IIWSCOA-based DCNN approach outperforms other conventional approaches with an accuracy of 0.9412, sensitivity of 0.9535, and specificity of 0.9485.

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Section: Design and Development Of Hybrid Optimization-enabled Deep L...mentioning

confidence: 99%