Publication Type:

Journal Article

Source:

Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 28, p.734-743 (2018)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042417898&doi=10.1007%2f978-3-319-71767-8_64&partnerID=40&md5=314ac0ce829b5800a386a0de9fab8e90

Abstract:

<p>In the field of medical imaging, Ultrasonography is a popular and most frequently used diagnostic tool owing to its hazard-free, non–invasive and the cost effective nature. Liver being the largest and vital organ in the human body, liver disorders are treated very important and initial detection of the disorder is made using ultrasound imaging by the radiologists that leads to additional biopsies for confirmation, if necessary. This work focusses on the automated classification of nine types of both focal and diffused liver disorders using ultrasound images. A deep convolutional neural network architecture codenamed Inception is used. The technique achieves a new state for classification and detection of liver disease. The disease is predicted based on the score obtained as a result of training. The classification is achieved using tensor flow and it outputs the predicted labels and the corresponding scores. The method achieves reasonable accuracy using the trained model. © 2018, Springer International Publishing AG.</p>

Notes:

cited By 0

Cite this Research Publication

R. K. Krishnan, Midhila, M., and Sudhakar, R., “Tensor flow based analysis and classification of liver disorders from ultrasonography images”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 734-743, 2018.

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