METHOD FOR DETECTING COVID-19 INFECTION IN CHEST RADIOGRAPHS

Abstract

ABSTRACT A method (100) for detecting COVID-19 infection in chest radiographs. Further, the method collecting a dataset of chest radiographs, including images from both COVID-19 positive and negative cases. Further, the method (100) comprising the steps of pre-processing the collected chest radiographs to enhance image quality and standardize format, including normalization, resizing, and data augmentation. Further, the method (100) comprising the steps of training a convolutional neural network (CNN) model on the pre-processed dataset to identify distinguishing features indicative of COVID-19 infection. Further, the method (100) comprising the steps of evaluating the trained CNN model using independent validation datasets to assess its diagnostic accuracy, sensitivity, and specificity. Further, the method (100) comprising the steps of deploying the trained CNN model for real-time inference on new chest radiographs to provide diagnostic predictions

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of medical imaging and artificial intelligence, and in particular relates to a method for detecting COVID-19 infection in chest radiographs using deep learning techniques, specifically convolutional neural networks (CNNs), to enhance diagnostic accuracy and efficiency in clinical settings.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The COVID-19 pandemic has significantly impacted global health systems, necessitating the rapid and , Claims:1. A method (100) for detecting COVID-19 infection in chest radiographs, the method comprising the steps of:
collecting a dataset of chest radiographs, including images from both COVID-19 positive and negative cases;
pre-processing the collected chest radiographs to enhance image quality and standardize format, including normalization, resizing, and data augmentation;
training a convolutional neural network (CNN) model on the pre-processed dataset to identify distinguishing features indicative of COVID-19 infection;
evaluating the trained CNN model using independent validation datasets to assess its diagnostic accuracy, sensitivity, and specificity; and
deploying the trained CNN model for real-time inference on new chest radiographs to provide diagnostic predictions regarding the presence of COVID-19.

2. The method (100) as claimed in claim 1, further comprising optimizing the architecture, parameters, and hyper parameters of the CNN model through techniques including hyper parameter tuning and transfer

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