Abstract: The COVID-19 pandemic has created an urgent need for efficient and accurate diagnostic methods. X-ray imaging has been widely used in detecting COVID-19, however, the interpretation of X-ray images requires expertise and is prone to errors. In order to improve the accuracy of COVID-19 diagnosis using X-ray images, a detection system can be developed using a combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The proposed system will aim to automatically detect COVID-19 from X-ray images with a high level of accuracy. The proposed system will use a pre-trained CNN model to extract features from the input X-ray images. The extracted features will then be fed into an RNN to capture the temporal information in the image sequences. The RNN will be trained to classify the X-ray images into two categories: COVID-19 positive and negative. The system will be trained using a large dataset of X-ray images from COVID-19 positive and negative patients. The dataset will be divided into training, validation, and testing sets. The system will be optimized using a loss function and backpropagation algorithm. The performance of the system will be evaluated using various metrics such as accuracy, sensitivity, and specificity. The system will also be compared with other state-of-the-art methods for COVID-19 detection from X-ray images. Overall, the proposed system has the potential to provide an efficient and accurate method for COVID-19 detection using X-ray images.
The outbreak of COVID-19 has created an urgent need for effective and efficient diagnostic methods. This project proposes a COVID-19 detection system that utilizes a combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to automatically identify COVID-19 from X-ray images with high accuracy. The system extracts features from the input X-ray images using a pre-trained CNN model, and then employs an RNN to capture the temporal information. The RNN is trained to classify X-ray images into COVID-19 positive and negative categories using a large dataset of COVID-19 positive and negative patients. The system is optimized using a loss function and backpropagation algorithm, and its performance is evaluated using various metrics such as accuracy, sensitivity, and specificity. The proposed system has the potential to provide an efficient and accurate method for COVID-19 detection using X-ray images.
Keywords: Convolutional Neural Network, Recurrent Neural Network, chest X-rays, LSTM,
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DOI:
10.17148/IJARCCE.2023.12430
