Abstract
The chest X-ray is among the most widely used diagnostic imaging for diagnosing many lung and bone-related diseases. Recent advances in deep learning have shown many good performances in disease identification from chest X-rays. But stability and class imbalance are yet to be addressed. In this study, we proposed a CX-Ultranet (Chest X-ray Ultranet) to classify and identify thirteen thoracic lung diseases from chest X-rays by utilizing a multiclass cross-entropy loss function on a compound scaling framework using EfficientNet as a baseline. The CX-Ultra net achieves 88% average prediction accuracy on NIH Chest X-ray Dataset. It takes ≈ 30% less time than pre-existing state-of-the-art models. The proposed CX-Ultra net gives higher average accuracy and efficiently handles the class imbalance issue. The training time in terms of Floating-Point Operations Per Second is significantly less, thus setting a new threshold in disease diagnosis from chest X-rays.
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Acknowledgement
This research work was supported by the RFIER-Jio Institute “CVMI-Computer Vision in Medical Imaging” research project fund under the “AI for ALL” research center.
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Kabiraj, A., Meena, T., Reddy, P.B., Roy, S. (2022). Detection and Classification of Lung Disease Using Deep Learning Architecture from X-ray Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13598. Springer, Cham. https://doi.org/10.1007/978-3-031-20713-6_34
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DOI: https://doi.org/10.1007/978-3-031-20713-6_34
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