A Multi-Modal CNN-based Approach for COVID-19 Diagnosis using ECG, X-Ray, and CT

Controlling the spread of Coronavirus Disease 2019 (COVID-19) and reducing its impact on public health need prompt identification and treatment. To improve diagnostic accuracy, this study attempts to create and assess a Multi-Modality COVID-19 Diagnosis System that integrates X-ray, Electrocardiogram (ECG), and Computed Tomography (CT) images utilizing Convolutional Neural Network (CNN) algorithms. To increase the accuracy of COVID-19 diagnosis, the suggested system incorporates data from many imaging modalities in a novel way, including cardiac symptoms identified by ECG data. This approach has not been thoroughly studied in the literature to date. The system analyses CT, ECG, and X-ray images using CNN algorithms, including Visual Geometry Group 19 (VGG19) and Deep Convolutional Networks (DCNN). While ECG data helps detect related cardiac symptoms, CT and X-ray images offer precise insights into lung abnormalities indicative of COVID-19 pneumonia. Noise reduction and image smoothing are accomplished through the implementation of Gaussian filtering algorithms. After extracting characteristics suggestive of either bacterial or viral pneumonia, a deep neural network refines them for accurate COVID-19 identification. Python software is employed throughout the system's implementation. A thorough evaluation of the trained CNN model using separate datasets revealed an amazing 99.12% accuracy rate in COVID-19 detection from chest imaging data. The diagnostic accuracy of the suggested DCNN model was much higher than that of the current models, including Random Forest and Linear Ridge. The Multi-Modality COVID-19 Diagnosis System uses cutting-edge CNN algorithms to seamlessly combine ECG, X-ray, and CT imaging data to provide a highly accurate diagnosis tool. With the implementation of this approach, medical personnel could potentially be able to diagnose COVID-19 more quickly and accurately, which would improve the disease's treatment and control.

@article{Keshamoni2024,
  title     = {A Multi-Modal CNN-based Approach for COVID-19 Diagnosis using ECG, X-Ray, and CT},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Kumar Keshamoni and L Koteswara Rao and D. Subba Rao},
  doi       = {10.14569/IJACSA.2024.01506112},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506112}
}