Details

Autor(en) / Beteiligte

• Arul Raj, A.M
• Sugumar, R

Titel

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

Ist Teil von

• 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), 2023, p.569-575

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Xplore

Beschreibungen/Notizen

• 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.