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A deep learning-based system for accurate diagnosis of pelvic bone tumors.
- Source :
- Bulletin of Electrical Engineering & Informatics; Jun2024, Vol. 13 Issue 3, p1802-1813, 12p
- Publication Year :
- 2024
-
Abstract
- Bone image analysis and categorizing bone cancers have both seen advancements thanks to deep learning (DL), more notably convolution neural networks (CNN). This study suggests a brand-new CNN-based methodology for categorizing pelvic bone tumors specifically. This work aims to create a pelvic bone computed tomography (CT) image categorization system based on deep learning. The proposed technique uses a convolutional neural network (CNN) architecture to automatically extract information from the CT images and classify them into distinct categories of tumors. A total of 178 3D CT pictures was discovered and added retroactively. DenseNet created the image-based model with Adam optimizer and cross entropy loss. The suggested system's accuracy is assessed using a variety of performance indicators, including sensitivity, specificity, and F1-score. As demonstrated by the experiment findings, the suggested deep learning based classification system has a high degree of accuracy (94%), making it useful for the diagnosis and treatment of pelvic bone tumors. Our promising results might hasten the use of DL-assisted CT diagnosis for pelvic bone tumors in the future. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20893191
- Volume :
- 13
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Bulletin of Electrical Engineering & Informatics
- Publication Type :
- Academic Journal
- Accession number :
- 177957705
- Full Text :
- https://doi.org/10.11591/eei.v13i3.6861