Reducing False Prediction On COVID-19 Detection Using Deep Learning
Biswajit Bhowmik, Shrinidhi Anil Varna, Adarsh Kumar, Rahul Kumar
Abstract
This paper proposes a custom deep neural network-based scheme for coronavirus disease 2019 (COVID-19) detection. The proposed method takes X-ray images that use transfer learning techniques on pre-trained models. One objective of this work is to quickening the detection of the virus. Another goal is to reduce the number of falsely detected cases by a significant margin. The experimental setup demonstrates promising results on the selected dataset, which achieve up to 99.74%, 99.69%, 98.80% as classification, precision, and recall accuracy.
Topics & Concepts
Margin (machine learning)Coronavirus disease 2019 (COVID-19)Computer scienceArtificial intelligenceTransfer of learningDeep learningPrecision and recallArtificial neural networkMachine learningRecallQuickeningPattern recognition (psychology)DiseaseMedicinePathologyPhilosophyRadiologyLinguisticsInfectious disease (medical specialty)COVID-19 diagnosis using AIAnomaly Detection Techniques and ApplicationsAI in cancer detection