Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach.

ABSTRACT: Chest X-rays are the most economically viable diagnostic imaging test for active pulmonary tuberculosis screening despite the high sensitivity and low specificity when interpreted by clinicians or radiologists. Computer aided detection (CAD) algorithms, especially convolution based deep learning architecture, have been proposed to facilitate the automation of radiography imaging modalities. Deep learning algorithms have found success in classifying various abnormalities in lung using chest X-ray. We fine-tuned, validated and tested EfficientNetB4 architecture and utilized the transfer learning methodology for multilabel approach to detect lung zone wise and image wise manifestations of active pulmonary tuberculosis using chest X-ray. We used Area Under Receiver Operating Characteristic (AU

SUBMITTER: Devasia J 

PROVIDER: S-EPMC9845381 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature