ABSTRACT
Lung cancer is one of the leading causes of death worldwide. Its early detection in its nodular form is extremely effective in improving patient survival rate. Deep learning (DL) and especially Convolutional Neural Network (CNN) have an important development over the past decade and were largely explored in medical imaging analysis. In this paper, a trending DL model composed of two CNN streams, named Bilinear CNN (B-CNN), was proposed for lung nodules classification on CT scans. In the developed B-CNN model, the pre-trained VGG16 architecture was trained as a feature extractor. It is the most important part of the proposed model in which its effectiveness depends stringently on its performances. Aiming to improve these performances, we address this question: what process leads with the performance improvement of the feature extractors? Transfer learning or Fine-tuning? To answer this question, two B-CNN models were implemented, in which the first one was based on transfer learning process and the second was based on fine-tuning, using VGG16 networks. A set of experiments was conducted and the results have shown the outperformance of the fine-tuned B-CNN model compared to the transfer learning-based model. Moreover, the proposed B-CNN model was demonstrating its efficiency and viability for the classification of lung nodules in terms of accuracy and AUC compared to existing works.
- Cancer Facts & Figures 2020. Atlanta: American Cancer Society, Homepage: https://www.cancer.org/cancer/lung-cancer/prevention-and-early-detection.html, last accessed 2020/7/06.Google Scholar
- Lung Cancer Fact Sheet. American Lung Association, Homepage: http://www.lung.org/lung-health-and-diseases/lung-disease-lookup, last accessed 2020/7/20.Google Scholar
- Mastouri, R., Khlifa, N., Neji, H., Hantous-Zannad, S. 2020. Deep learning-based CAD schemes for the detection and classification of lung nodules from CT images: A survey. Journal of X-ray science and technology. 28(4), 591--617. DOI=https://doi.org/10.3233/XST-200660Google ScholarCross Ref
- Lin, T. Y., RoyChowdhury, A., Maji, S. 2015. Bilinear cnn models for fine-grained visual recognition. In: Proceedings of the IEEE international conference on computer vision. 1449--1457.Google ScholarDigital Library
- Zhao, X., Qi, S., Zhang, B., Ma, H., Qian, W., et al. 2019. Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning. Journal of X-ray science and technology. 27(4), 615--629.Google ScholarCross Ref
- Tran, G. S., Nghiem, T. P., Nguyen, V. T., et al. 2019. Improving accuracy of lung nodule classification using deep learning with focal loss. Journal of Healthcare Engineering. DOI = https://doi.org/10.1155/2019/5156416.Google Scholar
- Wu, P., Sun, X., Zhao, Z., Wang, H., Pan, S., et al. 2020. Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning. Computational Intelligence and Neuroscience. 1--10.Google Scholar
- Shi, Z., Hao, H., Zhao, M., Feng, Y., et al. 2019. A deep CNN based transfer learning method for false positive reduction. Multimedia Tools and Applications. 78(1), 1017--1033Google ScholarDigital Library
- Ben, J. M., Guetari, R., Chetouani, A., Tabia, H., Khlifa, N. 2020. Facial expression recognition using the bilinear pooling. In: 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP). 294--301. DOI=https://doi.org/10.5220/0008928002940301.Google Scholar
- Wang, C., Shi, J., Zhang, Q., Ying, S. 2017. Histopathological image classification with bilinear convolutional neural networks. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).Google ScholarCross Ref
- Setio, A. A. A., Traverso, A., De Bel, T., Berens, M. S., et al. 2017. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Medical image analysis. 42, 1--13.Google Scholar
- Armato III, S. G., McLennan, G., Bidaut, L., et al. 2011. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical physics. 38(2), 915--931.Google Scholar
- Kaya, A. 2018. Cascaded Classifiers and Stacking Methods for Classification of Pulmonary Nodule Characteristics. Computer Methods and Programs in Biomedicine. 166, 77--89.Google ScholarCross Ref
- López-Sánchez, D., González Arrieta, A., et al. 2019. Compact bilinear pooling via kernelized random projection for fine-grained image categorization on low computational power devices. Neurocomputing. DOI=https://doi.org/10.1016/j.neucom.2019.05.104Google Scholar
- Chen, H., Wang, J., Qi, Q., Li, Y., Sun, H. 2017. Bilinear cnn models for food recognition. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA). 1--6.Google ScholarCross Ref
- Rahmany, I., Guetari, R., Khlifa, N. 2018. A Fully Automatic based Deep Learning Approach for Aneurysm Detection in DSA Images. In: IEEE International Conference on Image Processing, Applications and Systems (IPAS).Google ScholarCross Ref
- Moussa, O., Khachnaoui, H., et al. 2019. Thyroid nodules classification and diagnosis in ultrasound images using fine-tuning deep convolutional neural network. International Journal of Imaging Systems and Technology.Google Scholar
Index Terms
- Transfer Learning Vs. Fine-Tuning in Bilinear CNN for Lung Nodules Classification on CT Scans
Recommendations
Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models
We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). ...
UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans
Image Analysis and Processing – ICIAP 2022AbstractLung cancer has emerged as a major causes of death and early detection of lung nodules is the key towards early cancer diagnosis and treatment effectiveness assessment. Deep neural networks achieve outstanding results in tasks such as lung nodules ...
Classification of Lung Nodules Based on GAN and 3D CNN
CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application EngineeringAn efficient model based on generative adversarial networks (GAN) and the three-dimensional convolutional neural network (3D CNN) is presented and implemented to classify lung nodules, useful for false positives removal. The model is trained on LIDC-...
Comments