An efficient novel approach for early detection of lung cancer through document image classification with distributed machine learning paradigms.

Recent advancements in deep learning have made it easier to identify and classify lung disorders in medical imaging. As a result, there are several works in the literature on the diagnosis of lung disorders using deep learning. The deep learning research for detecting lung illnesses in medical imaging is presented in this section. Only few research publications on deep learning for detecting lung disorders has been published in the recent five years. Early detection of coronavirus disease in 2019 (COVID-19) is crucial to control the pandemic. COVID-19 is spreading rapidly around the world. The use of computed tomography (CT) images enables rapid and accurate detection of COVID-19. The deep learning techniques of the proposed method are based on a Hadoop Distributed Deep Convolutional Neural Network (HdiDConvNNet) address the problem statement for early detection of lung diagnose. The purpose of our research is to use various distributed deep learning algorithms to distinguish CT scan images with COVID-19 and non-COVID 19. Clinicians can use automated COVID-19 diagnostics using CT scan images as a fast and efficient way to detect COVID - 19. Precision and Recall are both important factor for this research work, hence the results are analyzed in terms of F1-Score. The confusion matrix and results for the F1-Score, Precision, Recall, and overall Accuracy are also presented to provide a complete analysis of the Model performance. The proposed methods have had a significant impact in the country as a warning to society. Therefore, the existence of such a hypothesis for innovative research will significantly reduce the incidence of lung cancer deaths affected by Covid-19 and will be considered for prior public advice. [ABSTRACT FROM AUTHOR]