Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

The Coronavirus Disease (COVID-19) pandemic has exposed the vulnerabilities of medical services across the globe, especially in underdeveloped nations. In the aftermath of the COVID-19 outbreak, a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods. Medical imaging has become a crucial component in the disease diagnosis process, whereas X-rays and Computed Tomography (CT) scan imaging are employed in a deep network to diagnose the diseases. In general, four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks, such as network training, feature extraction, model performance testing and optimal feature selection. The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion (CFPADLDF) approach for detecting and classifying COVID-19. The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images. Initially, the proposed CFPA-DLDF technique employs the Gabor Filtering (GF) approach to pre-process the input images. In addition, a weighted voting-based ensemble model is employed for feature extraction, in which both VGG-19 and the MixNet models are included. Finally, the CFPA with Recurrent Neural Network (RNN) model is utilized for classification, showing the work's novelty. A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model, and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches. © 2023 Tech Science Press. All rights reserved.

COVID-19 classification using feature based deep learning approach

According to consensus, the use of Computerized Tomography (CT) methodology for early finding of several disease, yields both quick and reliable results. Expert radiologists reported that COVID19 has exhibit severalmanners in CT images. In this research, a novel technique of fusing and rankingfeatures based Deep Learning Approach was proposed to detect COVID-19 in its early stages. To create sub-datasets, 32x32 as Subset-1 and 64x64 as Subset-2, within the framework of the proposed procedure, 300 patch images as COVID-19 and Non-COVID-19 were used in the training and testing phases. A VB-Net Deep learning-based segmentation system was created to segment the infection regions in CT scans image of COVID-19 patients. To improve the proposed methodperformance, feature fusion and a ranking method were used.The Convolutional Neural Network (CNN) technique is used in transfer learning. The processed data was then categorized into two types as by using a Support Vector Machine (SVM). This study compares the proposed two subsets with different CNN architecture as Resnet-50, VGG-16 and GoogleNet performance result.By this comparison, the proposed model Subset-2 achieved a better accuracy of 97.58% than other comparison models.