Machine Learning Based Psychological Disease Support Model Assisting Psychoanalysts and Individuals in Clinical Decision Ministration

Mohammadian, Masoud; Kamra, Vikas; Kumar, Praveen

doi:10.12785/ijcds/090414

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…In their works, Rahimzadeh et al [24] have inferred that CXRs are better than some other means of identifying COVID-19 due to their promising outcomes alongside the accessibility of CXR machines and their low support cost. Much research works have been done to identify COVID-19 with the help of CXR images [25]- [27]. As a result, Wang et al [28] proposed an automatic COVID-19 detection model using ensemble learning with a total dataset of 1,006 CXR images giving a final classification accuracy of 91.62%.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning based COVID and Pneumonia detection using chest X-ray

Kumar,

Rakhimzhanova,

Rawat

et al. 2024

IJEECS

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<p class="Abstract">Since the outbreak, the novel coronavirus (COVID-19) has infected more than 180 million people and has taken a toll of 3.91 million lives globally as of June 2021. This virus causes symptoms like fever, cold, and fatigue, and can develop into Pneumonia which can be detected using chest X-rays (CXRs). Therefore, early detection of COVID-19 can help get early medical attention. However, a sudden rise in the number of cases in many countries caused by COVID waves increases the burden on their testing facilities. As a result, they sometimes fail to perform enough testing to contain the spread. This work proposes a deep learning model to detect COVID-19 and Pneumonia based on CXRs. The dataset for our COVID model contains a total of 3,400 CXRs images of COVID-19 patients and 3,400 normal CXRs. The dataset for our Pneumonia model contains 1,300 CXR images of Pneumonia patients and 1,300 normal CXRs. We use convolutional neural network provided by TensorFlow to build our model, which gave 94.17% and 93.55% accuracy for COVID model and Pneumonia model, respectively. Finally, we deployed our model on the web and added a web tracker, which gives us the cases, deaths, and recoveries state-wise and nationwide.</p>

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