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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.6
- 발행연도
- 2023.12
- 수록면
- 502 - 510 (9page)
- DOI
- 10.5573/IEIESPC.2023.12.6.502
이용수
초록· 키워드
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Binarized spiking neural networks optimized with a color harmony algorithm for liver cancer classification (BSNN-CHA-LCC) are proposed to classify liver cancer as normal and abnormal. Initially, fusion of an MRI dataset and CT-scan datasets of a liver cancer dataset were taken, and the input images were given to CWF-based preprocessing for removing noise and increasing the quality of input computed tomography (CT) and magnetic resonance imaging (MRI). The preprocessed images of CT and MRI are given to improve the non-sub sampled Shearlet transform (INSST) method-based feature extraction for extracting features. The extracted features were given BSNN to classify liver cancer as normal and abnormal. The proposed method was implemented, and the efficiency of the proposed BSNN-CHA-LCC method was evaluated under performance metrics, such as precision, sensitivity, F-scores, specificity, accuracy, error rate, and computational time. The proposed technique achieved23.03%, 11.56%, and 21.22% higher accuracy and 36.12%, 15.23%, and 27.11% lower error rates than the existing models, such as hybrid-feature analysis depending on machine-learning for liver cancer categorization utilizing fused images (MLP-LCC), Deep learning-based classification of liver cancer histopathology images utilizing only global labels (mask-RCNN-LCC), and deep learning based liver cancer identification utilizing watershed transform and Gaussian mixture method (DNN-GMM-LCC), respectively.
목차
Abstract
1. Introduction
2. Literature Survey
3. Proposed Methodology
4. Results and Discussion
5. Conclusion
References
참고문헌 (18)
참고문헌 신청
M. Chen, B. Zhang, W. Topatana, J. Cao, H. Zhu, S. Juengpanich, Q. Mao, H. Yu, X. Cai,“ Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning,” NPJ precision oncology, vol. 4, no. 1, p. 14, 2020. Art
A. Kiani, B. Uyumazturk, P. Rajpurkar, A. Wang, R. Gao, E. Jones, Y. Yu, C.P Langlotz, R.L. Ball, T.J. Montine. B. A. Martin,“Impact of a deep learning assistant on the histopathologic classification of liver cancer,” NPJ digital medicine, vol. 3, no. 1,
P. Narmatha, S. Gupta, T. V. Lakshmi, D. Manikavelan, “Skin cancer detection from dermoscopic images using Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm,” Biomedical Signal Processing and Control, vol. 8
M. Fujita, R. Yamaguchi, T. Hasegawa, S. Shimada, K. Arihiro, S. Hayashi, K. Maejima, K. Nakano, A. Fujimoto, A. Ono, H. Aikata,“Classification of primary liver cancer with immunosuppression mechanisms and correlation with genomic alterations, EBioMedicin
A. Krishan, D. Mittal, “Ensembled liver cancer detection and classification using CT images,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 235, no. 2, pp. 232-244, 2021. Article (CrossRef Link)
A. Kiani, B. Uyumazturk, P. Rajpurkar, A. Wang, R. Gao, E. Jones, Y. Yu, C.P Langlotz, R.L. Ball, T.J. Montine. B. A. Martin,“Impact of a deep learning assistant on the histopathologic classification of liver cancer,” NPJ digital medicine, vol. 3, no. 1,
P. Narmatha, S. Gupta, T. V. Lakshmi, D. Manikavelan, “Skin cancer detection from dermoscopic images using Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm,” Biomedical Signal Processing and Control, vol. 8
M. Fujita, R. Yamaguchi, T. Hasegawa, S. Shimada, K. Arihiro, S. Hayashi, K. Maejima, K. Nakano, A. Fujimoto, A. Ono, H. Aikata,“Classification of primary liver cancer with immunosuppression mechanisms and correlation with genomic alterations, EBioMedicin
A. Krishan, D. Mittal, “Ensembled liver cancer detection and classification using CT images,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 235, no. 2, pp. 232-244, 2021. Article (CrossRef Link)
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