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ABSTRACT
We provide abstract of convolutional neural net in this paper.
Introduction: The ongoing global pandemic has heightened the need for efficient and rapid diagnostic tools. This study explores the utilization of Convolutional Neural Network (CNN) technology to enhance COVID-19 detection through X-ray imaging.
Methodology: In this research, a dataset comprising X-ray images of COVID-19 positive and negative cases was curated.
Data Preprocessing: Prior to training the CNN, meticulous data preprocessing was undertaken. This involved normalization, resizing, and augmentation techniques to ensure the model’s robustness and adaptability to diverse X-ray image characteristics.
CNN Architecture: The Convolutional Neural Network architecture was designed with multiple convolutional and pooling layers. These layers played a pivotal role in learning hierarchical features from X-ray images, enabling the network to make accurate distinctions between COVID-19 positive and negative cases.
Results and Evaluation: so Upon completion of the training phase, the CNN exhibited remarkable accuracy in differentiating COVID-19 cases from other respiratory conditions.
Conclusion: thus This research demonstrates the efficacy of Convolutional Neural Networks in enhancing the detection of COVID-19 through X-ray images. The proactive integration of deep learning technologies into diagnostic processes holds great promise for expediting and improving the accuracy of COVID-19 diagnoses.
In conclusion, the utilization of CNNs in conjunction with X-ray imaging presents a potent solution for efficient and reliable COVID-19 detection, contributing significantly to the global efforts to combat the pandemic.
