A Healthcare System for COVID19 Classification Using Multi-Type Classical Features Selection

The coronavirus (COVID19), also known as the novel coronavirus, first appeared in December 2019 in Wuhan, China. After that, it quickly spread throughout the world and became a disease. It has significantly impacted our everyday lives, the national and international economies, and public health. However, early diagnosis is critical for prompt treatment and reducing trauma in the healthcare system. Clinical radiologists primarily use chest X-rays, and computerized tomography (CT) scans to test for pneumonia infection. We used Chest CT scans to predict COVID19 pneumonia and healthy scans in this study. We proposed a joint framework for prediction based on classical feature fusion and PSO-based optimization. We begin by extracting standard features such as discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and dominant rotated local binary patterns (DRLBP). In addition, we extracted Shanon Entropy and Kurtosis features. In the following step, a Max-Covariance-based maximization approach for feature fusion is proposed. The fused features are optimized in the preliminary phase using Particle Swarm Optimization (PSO) and the ELM fitness function. For final prediction, PSO is used to obtain robust features, which are then implanted in a Support Vector Data Description (SVDD) classifier. The experiment is carried out using available COVID19 Chest CT Scans and scans from healthy patients. These images are from the Radiopaedia website. For the proposed scheme, the fusion and selection process accuracy is 88.6% and 93.1%, respectively. A detailed analysis is conducted, which supports the proposed system efficiency. © 2023 Tech Science Press. All rights reserved.

A Single Stream Modified MobileNet V2 and Whale Controlled Entropy Based Optimization Framework for Citrus Fruit Diseases Recognition

Fruit disease recognition is quickly becoming a hot topic in the field of computer vision. The presence of plant diseases not only reduces fruit production but also causes a significant loss to the national economy. Citrus fruits help to strengthen the immune system, allowing it to fight off diseases such as COVID-19. Manual inspection of fruit diseases with the naked eye takes time and is difficult;therefore, a computer based method is always required for accurate recognition of plant diseases. Several deep learning techniques for recognizing citrus fruit diseases have been introduced in the literature. Existing techniques had several issues, including redundant features, convolutional neural network (CNN) model selection, low contrast images, and long computational times. In this paper, single stream convolutional neural network architecture is proposed for recognizing citrus fruit diseases. In the first step, data augmentation is performed using four contrast enhancement operations: shadow removal, adjusting pixel intensity, improving brightness, and improving local contrast. The MobileNet-V2 CNN model is selected and fine-tuned in the second step. Using the transfer learning process, the fine-tuned model is trained on the augmented citrus dataset. The newly trained model is used for deep feature extraction;however, analysis shows that the extracted deep features contain little redundant information. As a result, an improved Whale Optimization Algorithm (IWOA) is used in the third step. The best features are then classified using machine learning classifiers in the final step. The augmented citrus fruits, leaves, and hybrid dataset were used in the experimental process and achieved an accuracy of 99.4, 99.5, and 99.7%. When compared to existing techniques, the proposed architecture outperformed them in terms of accuracy and time.

Classification of COVID-19 CT Scans via Extreme Learning Machine

Here, we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography (CT) scans. The scheme operates in four steps. Initially, we prepared a database containing COVID-19 pneumonia and normal CT scans. These images were retrieved from the Radiopaedia COVID-19 website. The images were divided into training and test sets in a ratio of 70:30. Then, multiple features were extracted from the training data. We used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive capacity. We next implemented a genetic algorithm (GA) in which an Extreme Learning Machine (ELM) served to assess GA fitness. Based on the ELM losses, the most discriminatory features were selected and saved as an ELM Model. Test images were sent to the model, and the best-selected features compared to those of the trained model to allow final predictions. Validation employed the collected chest CT scans. The best predictive accuracy of the ELM classifier was 93.9%;the scheme was effective.