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• W4289517812 abstract "To identify various pneumonia types, a gap of 15% value is being created every five years. To fill this gap, accurate detection of chest disease is required in the healthcare department to avoid any serious issues in the future. Testing the affected lungs to detect a Coronavirus 2019 (COVID-19) using the same imaging modalities may detect some other chest diseases. This wrong diagnosis strongly needs a multidisciplinary approach to the right diagnosis of chest-related diseases. Only a few works till now are targeting pathological x-ray images. Many studies target only a single chest disease that is not enough to automate chest disease detection. Only a few studies regarding the observation of the COVID-19, but more cases are those where it can be misclassified as detecting techniques not providing any generic solution for all types of chest diseases. However, the existing studies can only detect if the person has COVID-19 or not. The proposed work significantly contributes to detecting COVID-19 and other chest diseases by providing useful analysis of chest-related diseases. One of our testing approaches achieves 90.22% accuracy for 15 types of chest disease with 100% correct classification of COVID-19. Though it analyzes the perfect detection as the accuracy level is high enough, but it would be an excellent decision to consider the proposed study until doctors can visually inspect the input images used by models that lead to its detection." @default.
• W4289517812 created "2022-08-03" @default.
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• W4289517812 date "2022-08-03" @default.
• W4289517812 modified "2023-10-01" @default.
• W4289517812 title "AI-driven deep and handcrafted features selection approach for Covid-19 and chest related diseases identification" @default.
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• W4289517812 doi "https://doi.org/10.1007/s11042-022-13499-3" @default.
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