پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 399 |
| تعداد مقالات | 5,389 |
| تعداد مشاهده مقاله | 5,288,018 |
| تعداد دریافت فایل اصل مقاله | 4,882,766 |
Efficient deep learning algorithms for lower grade gliomas cancer MRI image segmentation: A case study | ||
| AUT Journal of Mathematics and Computing | ||
| مقاله 3، دوره 6، شماره 1، فروردین 2025، صفحه 19-30 اصل مقاله (890.4 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajmc.2023.22562.1172 | ||
| نویسندگان | ||
| AmirReza BabaAhmadi* 1؛ Zahra FallahPour2 | ||
| 1School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2School of Computer Engineering, College of Engineering, University of Shiraz, Shiraz, Iran | ||
| چکیده | ||
| This study explores the use of efficient deep learning algorithms for segmenting lower grade gliomas (LGG) in medical images. It evaluates various pre-trained atrous-convolutional architectures and U-Nets, proposing a novel transformer-based approach that surpasses traditional methods. DeepLabV3+ with MobileNetV3 backbone achieved the best results among pre-trained models, but the transformer-based approach excelled with superior segmentation accuracy and efficiency. Transfer learning significantly enhanced model performance on the LGG dataset, even with limited training samples, emphasizing the importance of selecting appropriate pre-trained models. The transformer-based method offers advantages such as efficient memory usage, better generalization, and the ability to process images of arbitrary sizes, making it suitable for clinical applications. These findings suggest that advanced deep learning techniques can improve diagnostic tools for LGG and potentially other cancers, highlighting the transformative impact of deep learning and transfer learning in medical image segmentation. | ||
| کلیدواژه‌ها | ||
| Brain tumor segmentation؛ Lower grade gliomas؛ Transformers؛ SegFormer؛ MRI images | ||
| مراجع | ||
|
[1] H. Andrew, S. Mark, C. Grace, C. Liang-Chieh, C. Bo, T. Mingxing, W. Weijun, Z. Yukun, P. Ruoming, V. Vijay, et al., Searching for mobilenetv3, in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 1314–1324. [2] R. Azad, M. Heidari, M. Shariatnia, E. K. Aghdam, S. Karimijafarbigloo, E. Adeli, and D. Merhof, Transdeeplab: Convolution-free transformer-based deeplab v3+ for medical image segmentation, in International Workshop on PRedictive Intelligence In MEdicine, Springer, 2022, pp. 91–102. [3] A. BabaAhmadi, S. Khalafi, and F. M. Esfahani, Designing an improved deep learning-based classifier for breast cancer identification in histopathology images, in 2022 International Conference on Machine Vision and Image Processing (MVIP), IEEE, 2022, pp. 1–4. [4] A. BabaAhmadi, S. Khalafi, M. ShariatPanahi, and M. Ayati, Designing an improved deep learningbased model for covid-19 recognition in chest x-ray images: A knowledge distillation approach, arXiv preprint arXiv:2301.02735, (2023). [5] B. Billot, D. Greve, K. Van Leemput, B. Fischl, J. E. Iglesias, and A. V. Dalca, A learning strategy for contrast-agnostic mri segmentation, arXiv preprint arXiv:2003.01995, (2020). [6] M. Buda, E. A. AlBadawy, A. Saha, and M. A. Mazurowski, Deep radiogenomics of lower-grade gliomas: Convolutional neural networks predict tumor genomic subtypes using mr images, Radiology: Artificial Intelligence, 2 (2020), p. e180050. [7] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 801–818. [8] R. Hua, Q. Huo, Y. Gao, H. Sui, B. Zhang, Y. Sun, Z. Mo, and F. Shi, Segmenting brain tumor using cascaded v-nets in multimodal mr images, Frontiers in Computational Neuroscience, 14 (2020), p. 9. [9] C. Huang and M. Wan, Automated segmentation of brain tumor based on improved u-net with residual units, Multimedia Tools and Applications, 81 (2022), pp. 12543–12566. [10] V. Iglovikov and A. Shvets, Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation, arXiv preprint arXiv:1801.05746, (2018). [11] S. Lefkovits, L. Lefkovits, and L. Szilagyi ´ , Hgg and lgg brain tumor segmentation in multi-modal mri using pretrained convolutional neural networks of amazon sagemaker, Applied Sciences, 12 (2022), p. 3620. [12] X. Li, X. Fang, G. Yang, S. Su, L. Zhu, and Z. Yu, Transu²-net: An effective medical image segmentation framework based on transformer and u²-net, IEEE Journal of Translational Engineering in Health and Medicine, 11 (2023), pp. 441–450. [13] S. Mishra and M. Manish, Studies on computational grafting of malarial epitopes in serum albumin, Computers in Biology and Medicine, 102 (2018), pp. 126–131. [14] H. Mzoughi, I. Njeh, A. Wali, M. B. Slima, A. BenHamida, C. Mhiri, and K. B. Mahfoudhe, Deep multi-scale 3d convolutional neural network (cnn) for mri gliomas brain tumor classification, Journal of Digital Imaging, 33 (2020), pp. 903–915. [15] O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer, 2015, pp. 234–241. [16] H. Saleem, A. R. Shahid, and B. Raza, Visual interpretability in 3d brain tumor segmentation network, Computers in Biology and Medicine, 133 (2021), p. 104410. [17] M. Tan and Q. Le, Efficientnet: Rethinking model scaling for convolutional neural networks, in International conference on machine learning, PMLR, 2019, pp. 6105–6114. [18] S. Vidyadharan, B. V. V. S. N. Prabhakar Rao, Y. Perumal, K. Chandrasekharan, and V. Rajagopalan, Deep learning classifies low-and high-grade glioma patients with high accuracy, sensitivity, and specificity based on their brain white matter networks derived from diffusion tensor imaging, Diagnostics, 12 (2022), p. 3216. [19] E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, Segformer: Simple and efficient design for semantic segmentation with transformers, Advances in Neural Information Processing Systems, 34 (2021), pp. 12077–12090. [20] D. Xu, X. Zhou, X. Niu, and J. Wang, Automatic segmentation of low-grade glioma in mri image based on unet++ model, 1693 (2020), p. 012135. [21] J. Zhang, Y. Liu, Q. Wu, Y. Wang, Y. Liu, X. Xu, and B. Song, Swtru: Star-shaped window transformer reinforced u-net for medical image segmentation, Computers in Biology and Medicine, 150 (2022), p. 105954. | ||
|
آمار تعداد مشاهده مقاله: 420 تعداد دریافت فایل اصل مقاله: 125 |
||