A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition

The outbreak of the SARS-CoV-2/Covid-19 virus in 2019-2020 has made the world look for fast and accurate detection methods of the disease. The most commonly used tools for detecting Covid patients are Chest-X-ray or Chest-CT-scans of the patient. However, sometimes it's hard for the physicians to diagnose the SARS-CoV-2 infection from the raw image. Moreover, sometimes, deep-learning-based techniques, using raw images, fail to detect the infection. Hence, this paper represents a hybrid method employing both traditional signal processing and deep learning technique for quick detection of SARS-CoV-2 patients based on the CT-scan and Chest-X-ray images of a patient. Unlike the other AI-based methods, here, a CT-scan/Chest-X-ray image is decomposed by two-dimensional Empirical Mode Decomposition (2DEMD), and it generates different orders of Intrinsic Mode Functions (IMFs). Next, The decomposed IMF signals are fed into a deep Convolutional Neural Network (CNN) for feature extraction and classification of Covid patients and Non-Covid patients. The proposed method is validated on three publicly available SARS-CoV-2 data sets using two deep CNN architectures. In all the databases, the modified CT-scan/Chest-X-ray image provides a better result than the raw image in terms of classification accuracy of two fundamental CNNs. This paper represents a new viewpoint of extracting preprocessed features from the raw image using 2DEMD.