Early Exit Based on Deep Learning Model for Polyp Colonoscopy Image Classification
Corressponding author's email:
nguyenhoanglong@hdu.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1721Keywords:
Early Exit, Deep Learning, Polyp, Classification, Computational efficiencyAbstract
Early exit is a widely adopted approach to reduce the inference time of deep learning models. By introducing side-branch classifiers into the main backbone network, this approach allows test samples to be predicted and exit the network early when high confidence is achieved. While the early exit mechanism has been extensively explored in various computer vision applications, its use in medical imaging remains relatively underexplored. In this study, we propose to design a lightweight early exit branch for polyp colonoscopy image classification with a combination of Convolutional Block Attention Module (CBAM) and Fully Connected Layer (FC). These branches are embedded into a deep learning backbone to leverage intermediate features for early predictions. Extensive experiments on the Kvasir polyp dataset demonstrate that our method achieves a favorable trade-off between accuracy and computational efficiency, showcasing its potential of lightweight early exit mechanisms to improve the efficiency of deep learning systems in medical image analysis, paving the way for faster and more resource-efficient diagnostic tools.
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B. Rokh, A. Azarpeyvand, and A. Khanteymoori, "A comprehensive survey on model quantization for deep neural networks in image classification," ACM Trans. Intell. Syst. Technol., vol. 14, no. 6, p. 50, 2023. DOI: https://doi.org/10.1145/3623402
A. Gholami, S. Kim, Z. Dong, Z. Yao, M. W. Mahoney, and K. Keutzer, "A survey of quantization methods for efficient neural network inference," in Low-Power Computer Vision, Chapman and Hall/CRC, 2022, pp. 291-326. DOI: https://doi.org/10.1201/9781003162810-13
A. Alqahtani, X. Xie, M. W. Jones, and E. Essa, "Pruning CNN filters via quantifying the importance of deep visual representations," Comput. Vis. Image Underst., vol. 208, p. 103220, 2021. DOI: https://doi.org/10.1016/j.cviu.2021.103220
Y. Zhang et al., "Advancing model pruning via bi-level optimization," in Advances in Neural Information Processing Systems, 2022.
C. H. Wang, K. Y. Huang, Y. Yao, J. C. Chen, H. H. Shuai, and W. H. Cheng, "Lightweight deep learning: An overview," IEEE Consum. Electron. Mag., 2022.
L. Beyer, X. Zhai, A. Royer, L. Markeeva, R. Anil, and A. Kolesnikov, "Knowledge distillation: A good teacher is patient and consistent," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2022. DOI: https://doi.org/10.1109/CVPR52688.2022.01065
S. Teerapittayanon, B. McDanel, and H. T. Kung, "Branchynet: Fast inference via early exiting from deep neural networks," in Proc. 23rd Int. Conf. Pattern Recognit. (ICPR), 2016. DOI: https://doi.org/10.1109/ICPR.2016.7900006
Y. Matsubara, M. Levorato, and F. Restuccia, "Split computing and early exiting for deep learning applications: Survey and research challenges," ACM Comput. Surv., vol. 55, no. 5, pp. 1-30, 2022. DOI: https://doi.org/10.1145/3527155
N. Passalis, J. Raitoharju, A. Tefas, and M. Gabbouj, "Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits," Pattern Recognit., vol. 105, p. 107346, 2020. DOI: https://doi.org/10.1016/j.patcog.2020.107346
H. Li, H. Zhang, X. Qi, R. Yang, and G. Huang, "Improved techniques for training adaptive deep networks," in Proc. IEEE/CVF Int. Conf. Comput. Vis., 2019. DOI: https://doi.org/10.1109/ICCV.2019.00198
M. Phuong and C. H. Lampert, "Distillation-based training for multi-exit architectures," in Proc. IEEE/CVF Int. Conf. Comput. Vis., 2019. DOI: https://doi.org/10.1109/ICCV.2019.00144
T. Bolukbasi, J. Wang, O. Dekel, and V. Saligrama, "Adaptive neural networks for efficient inference," in Proc. Int. Conf. Mach. Learn., 2017.
J. Shen et al., "Fractional skipping: Towards finer-grained dynamic CNN inference," in Proc. AAAI Conf. Artif. Intell., 2020. DOI: https://doi.org/10.1609/aaai.v34i04.6025
G. Huang et al., "Multi-scale dense networks for resource efficient image classification," arXiv preprint arXiv:1703.09844, 2017.
T. K. Hu, T. Chen, H. Wang, and Z. Wang, "Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference," arXiv preprint arXiv:2001.03460, 2020.
S. H. Gao et al., "Res2net: A new multi-scale backbone architecture," IEEE Trans. Pattern Anal. Mach. Intell., 2019.
H. Zhang et al., "ResNest: Split-attention networks," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2022. DOI: https://doi.org/10.1109/CVPRW56347.2022.00309
C. Srinivas et al., "Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images," J. Healthc. Eng., vol. 2022, p. 3264367, 2022. DOI: https://doi.org/10.1155/2022/3264367
Z. Liu et al., "Diagnosis of Alzheimer’s disease via an attention-based multi-scale convolutional neural network," Knowl.-Based Syst., vol. 238, p. 107942, 2022. DOI: https://doi.org/10.1016/j.knosys.2021.107942
J. Cheng et al., "ResGANet: Residual group attention network for medical image classification and segmentation," Med. Image Anal., vol. 76, p. 102313, 2022. DOI: https://doi.org/10.1016/j.media.2021.102313
A. Vaswani et al., "Attention is all you need," in Adv. Neural Inf. Process. Syst., 2017.
A. Dosovitskiy et al., "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929.
Y. Zhang, H. Liu, and Q. Hu, "Transfuse: Fusing transformers and CNNs for medical image segmentation," in Med. Image Comput. Comput.-Assist. Interv. (MICCAI), 2021. DOI: https://doi.org/10.1007/978-3-030-87193-2_2
Y. Dai, Y. Gao, and F. Liu, "TransMed: Transformers advance multi-modal medical image classification," Diagnostics, vol. 11, no. 8, p. 1384, 2021. DOI: https://doi.org/10.3390/diagnostics11081384
X. Huo et al., "HiFuse: Hierarchical multi-scale feature fusion network for medical image classification," Biomed. Signal Process. Control, vol. 87, p. 105534, 2024. DOI: https://doi.org/10.1016/j.bspc.2023.105534
S. Woo, J. Park, J. Y. Lee, and I. S. Kweon, "CBAM: Convolutional block attention module," in Proc. Eur. Conf. Comput. Vis. (ECCV), 2018. DOI: https://doi.org/10.1007/978-3-030-01234-2_1
K. Pogorelov et al., "KVASIR: A multi-class image dataset for computer-aided gastrointestinal disease detection," in Proc. 8th ACM Multimed. Syst. Conf., 2017. DOI: https://doi.org/10.1145/3193289
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