The Advantage of Transfer Learning with Pre-Trained Model in CNN Towards Ct-Scan Classification

Jasman Pardede(1*), Adwityo S. Purohita(2),

(1) Informatika - Institut Teknologi Nasional (Itenas) Bandung
(2) Informatika - Institut Teknologi Nasional (Itenas) Bandung
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v9i2.19872

Abstract

Medical image classification plays significant role in the process of medical decisions making, especially during the difficult period of the pandemic. One method being considered good at such classification is Convolutional Neural Network, in which we use pre-trained model approach with transfer learning since the limitation of medical images may require optimal effort. Through this pre-trained model with transfer learning, the objective is to maximize the accuracy of classification and to push forward the training session throughout the comparison of both transfer learning and non-transfer learning based pre-train models. The first type provides average accuracy of 0.84 with approximate training time 0.54 hour while the latter shows the average result of accuracy as 0.74 with average training time 0.58 hour. As the result, the optimizations are 1.13x for accuracy and 1.1x for training time. EfficientNetV2 is one pre-trained model selected for this project, being exposed to both transfer learning and non-transfer learning approach systems. The transfer learning version provides the superior accuracy as 0.88 and training time as 31 minutes - 50 seconds, showing the accuracy of 0.94 on validation and 0.88 on testing

Keywords

CT-Scan; CNN; Classification; EfficientNetV2; Transfer Learning

Full Text:

PDF

References

C. Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” The Lancet, vol. 395, no. 10223, pp. 497–506, Feb. 2020, doi: 10.1016/S0140-6736(20)30183-5.

Z. Y. Zu et al., “Coronavirus Disease 2019 (COVID-19): A Perspective from China,” Radiology, vol. 296, no. 2. Radiological Society of North America Inc., pp. E15–E25, Aug. 01, 2020. doi: 10.1148/radiol.2020200490.

E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” medRxiv, p. 2020.04.24.20078584, Jan. 2020, doi: 10.1101/2020.04.24.20078584.

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, May 27, 2015. doi: 10.1038/nature14539.

Y. Lecun, K. Kavukcuoglu, and C. Farabet, “Convolutional Networks and Applications in Vision,” 2010. [Online]. Available: http://www.cs.nyu.edu/

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” 2012. [Online]. Available: http://code.google.com/p/cuda-convnet/

C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A Survey on Deep Transfer Learning,” Aug. 2018, [Online]. Available: http://arxiv.org/abs/1808.01974

A. Howard et al., "Searching for mobilenetv3," in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314-1324, 2019.

M. Tan and Q. v. Le, “EfficientNetV2: Smaller Models and Faster Training,” Apr. 2021, [Online]. Available: http://arxiv.org/abs/2104.00298

M. Tan and Q. v. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” May 2019, [Online]. Available: http://arxiv.org/abs/1905.11946

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” Aug. 2016, [Online]. Available: http://arxiv.org/abs/1608.06993

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,” 2017. [Online]. Available: www.aaai.org

F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” Oct. 2016, [Online]. Available: http://arxiv.org/abs/1610.02357

Md Zahangir Alom, et al., "AlexNet," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018,

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv preprint arXiv:1704.04861, 2017.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, & L. C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, 2019.

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in International Conference on Machine Learning, pp. 448-456, June 2015.

C. Szegedy, V. Vanhoucke, S. Ioffe, and J. Shlens, “Rethinking the Inception Architecture for Computer Vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016.

C. Szegedy et al., “Going Deeper with Convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.1556

J. Pardede and A. S. Purohita, “Hyperparameter Search for CT-Scan Classification Using Hyperparameter Tuning in Pre-Trained Model CNN With MLP,” in 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM), Oct. 2022, pp. 1–8. doi: 10.1109/ICOSNIKOM56551.2022.10034878.

C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, no. 1, Dec. 2019, doi: 10.1186/s40537-019-0197-0.

R. Balestriero, L. Bottou, and Y. LeCun, “The Effects of Regularization and Data Augmentation are Class Dependent,” Apr. 2022, [Online]. Available: http://arxiv.org/abs/2204.03632

N. Srivastava, G. Hinton, A. Krizhevsky, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.

G. Hinton, Srivasta Nitish, and Swersky Kevin, “RMSProp,” 2013.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Dec. 2014, [Online]. Available: http://arxiv.org/abs/1412.6980

P. Ramachandran, B. Zoph, and Q. V. Le, “Searching for Activation Functions,” Oct. 2017, [Online]. Available: http://arxiv.org/abs/1710.05941

Y. Bengio, “Practical recommendations for gradient-based training of deep architectures,” Jun. 2012, [Online]. Available: http://arxiv.org/abs/1206.5533

X. Han et al., “Pre-trained models: Past, present and future,” AI Open, vol. 2, pp. 225–250, 2021, doi: https://doi.org/10.1016/j.aiopen.2021.08.002.

J. Pardede, B. Sitohang, S. Akbar, and M. L. Khodra, “Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection,” International Journal of Intelligent Systems and Applications, vol. 13, no. 2, pp. 52–61, Apr. 2021, doi: 10.5815/ijisa.2021.02.04.

B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” Jul. 2017, [Online]. Available: http://arxiv.org/abs/1707.07012

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Computer Vision and Pattern Recognition," in CVPR 2009, IEEE Conference on, 20-25 June 2009, IEEE, 2009.

Article Metrics

Abstract view(s): 225 time(s)
PDF: 114 time(s)

Refbacks

  • There are currently no refbacks.