Ensemble Transfer Learning-Based Convolutional Neural Network for Kidney Segmentation
Ensemble Transfer Learning-Based Convolutional Neural Network for Kidney Segmentation |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-9 |
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| Year of Publication : 2024 | ||
| Author : S. Nagarajan, M. Ramprasath |
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| DOI : 10.14445/22315381/IJETT-V72I9P142 | ||
How to Cite?
S. Nagarajan, M. Ramprasath, "Ensemble Transfer Learning-Based Convolutional Neural Network for Kidney Segmentation," International Journal of Engineering Trends and Technology, vol. 72, no. 9, pp. 446-457, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I9P142
Abstract
Kidney segmentation is crucial for many medical applications, including disease diagnosis, planning treatment, and kidney-related disorders. Convolutional Neural Networks (CNN), which are specially designed to process the intricate and multidimensional features found in kidney images, are at the core of ensemble-based transfer learning. With the help of a carefully chosen dataset of annotated kidney images, the proposed CNN model is trained to identify different patterns and variances in the anatomy of the kidneys. Data augmentation techniques are utilized to improve the segmentation model’s generalization and robustness, which leads to better performance on unseen data. In addition to deep learning, a preprocessing pipeline is integrated into the framework to enhance image quality, remove noise, and address potential artefacts that may hinder accurate segmentation. The combination of preprocessing steps and the CNN model results in precise and reliable kidney segmentations. The suggested method is thoroughly assessed using a variety of datasets, and its effectiveness is contrasted with current cutting-edge techniques. The results demonstrate how effectively the recommended method segments of kidneys in the image modalities and anatomical variations. The results of segmentation are quantitatively assessed using established metrics, showcasing the robustness and reliability of the developed approach. Furthermore, the proposed methodology’s potential clinical impact is highlighted through its application in aiding medical professionals in accurate diagnosis and treatment planning.
Keywords
Kidney tumour, CT imaging, Deep learning, Transfer learning, Convolutional Neural Networks (CNNs).
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