A deep learning based approach for classification of abdominal organs using ultrasound images

Santhosh Reddy, D. and Rajalakshmi, P. and Mateen, M.A. (2021) A deep learning based approach for classification of abdominal organs using ultrasound images. Biocybernetics and Biomedical Engineering, 41 (2). pp. 779-791. ISSN 02085216

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Ultrasound imaging is one of the primary modalities used for diagnosing a multitude of medical conditions affecting organs and soft tissues the body. Unlike X-rays, which use ionizing radiation, ultrasound imaging utilizes non-hazardous acoustic waves and is widely preferred by doctors. However, ultrasound imaging sometimes requires substantial manual effort in the identification of organs during real-time scanning. Also, it is a challenging task if the scanning performed by an unskilled clinician does not comprise adequate information about the organ, leading to an incorrect diagnosis and thereby fatal consequences. Hence, the automated organ classification in such scenarios can offer potential benefits. In this paper, We propose a convolutional neural network-based architecture (CNNs), precisely, a transfer learning approach using ResNet, VGG, GoogleNet, and Inception models for accurate classification of abdominal organs namely kidney, liver, pancreas, spleen, and urinary bladder. The performance of the proposed framework is analyzed using in-house developed dataset comprising of 1906 ultrasound images. Performance analysis shows that the proposed framework achieves a classification accuracy and F1 score of 98.77% and 98.55%, respectively, on an average. Also, we provide the performance of the proposed architecture in comparison with the state-of-the-art studies.

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IITH Creators:
IITH CreatorsORCiD
Santhosh Reddy, D.UNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Abdominal organ classification; Deep learning for medical imaging; Telehealth; Transfer learning; Ultrasound image
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 13 Jul 2021 04:42
Last Modified: 18 Feb 2022 06:13
URI: http://raiith.iith.ac.in/id/eprint/8252
Publisher URL: http://doi.org/10.1016/j.bbe.2021.05.004
OA policy: https://v2.sherpa.ac.uk/id/publication/35729
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