首页|Multi-Modal Fusion of Deep Learning with CNN based COVID-19 Detection and Classification Combining Chest X-ray Images

Multi-Modal Fusion of Deep Learning with CNN based COVID-19 Detection and Classification Combining Chest X-ray Images

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COVID-19 is a global pandemic that has caused a substantial increase in the number of people becoming sick and dying all across the globe。 The diagnosis of COVID-19 is essential for preventing the disease from becoming more widespread yet， it may be difficult to make owing to the many clinical presentations of the disease and the symptoms’ resemblance to those of other respiratory infections。 This research work， presents a multi-modal fusion technique for the detection and categorization of COVID-19 utilising chest X-ray pictures， CT scans， and RT-PCR testing。 In order to do the analysis and determine whether or not the images contain COVID-19， and make use of a deep learning strategy that incorporates a convolutional neural network (CNN)。 To further enhance the precision of the categorization， the proposed model additionally makes use of the findings from the RT-PCR test as an extra input。 The findings of the experiments indicate that the multi-modal fusion strategy that this work presented and delivers a greater level of accuracy than employing any particular modality by itself。

COVID-19Deep learningComputed tomographyComputational modelingData modelsConvolutional neural networksX-ray imaging

A.M Arul Raj.、R Sugumar

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Department of Computer and Science and Engineering, Saveetha School of Engineering (SSE), Saveetha Institute of Medical And Technical Sciences (SIMATS) Saveetha University, Chennai, India

International Conference on Intelligent Computing and Control Systems

Madurai(IN)

2023 7th International Conference on Intelligent Computing and Control Systems

569-575

2023

10.1109/ICICCS56967.2023.10142910