Abstract
In today’s world, many things have become feasible in the health sector due to the application of deep learning. One such application of deep learning is detecting diseases from chest X-ray images. As the X-ray images appear alike, identifying common thoracic diseases becomes the challenging task and thus requires a robust and faultless setup to achieve good results. This paper presents findings of experiment attempted using deep learning algorithms for detection and classification of common thoracic diseases from chest X-ray images. The experimentation consists of two-stage architecture where the first stage detects if there is any disease present in the chest and the second stage classifies the afflicted image in 14 different categories of diseases. TensorFlow, which is a state-of-the-art end-to-end platform for machine learning, is used for creating the architecture of this model. The classifier is created using convolutional neural network (CNN) and is trained on 112,120 images with resolution of 224 × 224. The original dataset is posted by the National Institute of Health (NIH) Clinical Center, USA. Training and validation are performed on this dataset, and the results are encouraging. The model successfully classifies different diseases present in the chest X-ray images while consuming considerably less time and resources.
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Fating, S., Kotambkar, D.M. (2022). Characterization of Common Thoracic Diseases from Chest X-ray Images Using CNN. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_51
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DOI: https://doi.org/10.1007/978-981-19-0840-8_51
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