Deep Feature Detection Approach for COVID-19 Classification based on X-ray Images

Abstract

The novel human Corona disease (COVID-19) is a pulmonary sickness brought on by an extraordinarily outrageous respiratory condition crown 2. (SARS -CoV-2). Chest radiography imaging has a significant role in the screening, early diagnosis, and follow-up of the suspected individuals due to the effects of COVID-19 on pneumonic-sensitive tissue. It also has a severe impact on the economy as a whole. If positive patients are identified early, the spread of the pandemic illness can be slowed. To determine whether people are at risk for illnesses, a COVID-19 infection prediction is critical. This paper categorizes chest CT samples of COVID-19 affected patients. The two-stage proposed deep learning technique produces spatial function from images, so it is a very expeditious manner for image category hassle. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) image datasets. Comparative evaluation reveals that our proposed method outperforms amongst other 20 different existing pre-trained models. The test outcomes constitute that our proposed model achieved the best rating of 97.6%, 0.964, 0.964, and 0.982 concerning the accuracy, precision, recall, specificity, and F1score, respectively.

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

“…Ambulance vehicles can be found using security cameras by employing machine learning models. A number of techniques, including a Motion History Image (MHI), K-Nearest Neighbor (KNN), Hidden Markov Model (HMM), Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), and others, can be used to automatically detect ambulance vehicles [1,[10][11][12][13][14][15].…”