Effective deep learning classification for kidney stone using axial computed tomography (CT) images
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Özlem Sabuncu
,
Bülent Bilgehan
,
Enver Kneebone
Abstract
Introduction
Stone formation in the kidneys is a common disease, and the high rate of recurrence and morbidity of the disease worries all patients with kidney stones. There are many imaging options for diagnosing and managing kidney stone disease, and CT imaging is the preferred method.
Objectives
Radiologists need to manually analyse large numbers of CT slices to diagnose kidney stones, and this process is laborious and time-consuming. This study used deep automated learning (DL) algorithms to analyse kidney stones. The primary purpose of this study is to classify kidney stones accurately from CT scans using deep learning algorithms.
Methods
The Inception-V3 model was selected as a reference in this study. Pre-trained with other CNN architectures were applied to a recorded dataset of abdominal CT scans of patients with kidney stones labelled by a radiologist. The minibatch size has been modified to 7, and the initial learning rate was 0.0085.
Results
The performance of the eight models has been analysed with 8209 CT images recorded at the hospital for the first time. The training and test phases were processed with limited authentic recorded CT images. The outcome result of the test shows that the Inception-V3 model has a test accuracy of 98.52 % using CT images in detecting kidney stones.
Conclusions
The observation is that the Inception-V3 model is successful in detecting kidney stones of small size. The performance of the Inception-V3 Model is at a high level and can be used for clinical applications. The research helps the radiologist identify kidney stones with less computational cost and disregards the need for many experts for such applications.
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Research funding: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Ethical approval: The local Institutional Review Board approved the study.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
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- Research frontiers of electroporation-based applications in cancer treatment: a bibliometric analysis
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- Deep neural network to differentiate internet gaming disorder from healthy controls during stop-signal task: a multichannel near-infrared spectroscopy study
- A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring
- Effective deep learning classification for kidney stone using axial computed tomography (CT) images
- De- and recellularized urethral reconstruction with autologous buccal mucosal cells implanted in an ovine animal model
- The impact of right ventricular hemodynamics on the performance of a left ventricular assist device in a numerical simulation model
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Artikel in diesem Heft
- Frontmatter
- Review
- Research frontiers of electroporation-based applications in cancer treatment: a bibliometric analysis
- Research Articles
- Deep neural network to differentiate internet gaming disorder from healthy controls during stop-signal task: a multichannel near-infrared spectroscopy study
- A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring
- Effective deep learning classification for kidney stone using axial computed tomography (CT) images
- De- and recellularized urethral reconstruction with autologous buccal mucosal cells implanted in an ovine animal model
- The impact of right ventricular hemodynamics on the performance of a left ventricular assist device in a numerical simulation model
- Optimal assist strategy exploration for a direct assist device under stress‒strain dynamics
- Revisiting SFA stent technology: an updated overview on mechanical stent performance
- Parameter-based patient-specific restoration of physiological knee morphology for optimized implant design and matching
- Influences of smart glasses on postural control under single- and dual-task conditions for ergonomic risk assessment
