• Ibomoiye Domor Mienye
  • Yanxia Sun
  • Zenghui Wang



convolutional neural network, deep learning, denseNet, predictive sparse decomposition


Lung cancer is the second most common form of cancer in both men and women. It is responsible for at least 25% of all cancer-related deaths in the United States alone. Accurate and early diagnosis of this form of cancer can increase the rate of survival. Computed tomography (CT) imaging is one of the most accurate techniques for diagnosing the disease. In order to improve the classification accuracy of pulmonary lesions indicating lung cancer, this paper presents an improved method for training a densely connected convolutional network (DenseNet). The optimized setting ensures that code prediction error and reconstruction error within hidden layers are simultaneously minimized. To achieve this and improve the classification accuracy of the DenseNet, we propose an improved predictive sparse decomposition (PSD) approach for extracting sparse features from the medical images. The sparse decomposition is achieved by using a linear combination of basis functions over the L2 norm. The effect of dropout and hidden layer expansion on the classification accuracy of the DenseNet is also investigated. CT scans of human lung samples are obtained from The Cancer Imaging Archive (TCIA) hosted by the University of Arkansas for Medical Sciences (UAMS).  The proposed method outperforms seven other neural network architectures and machine learning algorithms with a classification accuracy of 95%.


N. Emaminejad et al., “Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 5, pp. 1034–1043, May 2016.

A. P. Tafti, F. S. Bashiri, E. LaRose, and P. Peissig, “Diagnostic classification of lung CT images using deep 3D multi-scale convolutional neural network,” Proceedings of the 2018 IEEE International Conference on Healthcare Informatics (ICHI), 2018, pp. 412–414.

T. Guo, J. Dong, H. Li, and Y. Gao, “Simple convolutional neural network on image classification,” Proceedings of the 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 2017, pp. 721–724.

J. Ran, Y. Chen, and S. Li, “Three-dimensional convolutional neural network based traffic classification for wireless communications,” Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018, pp. 624–627.

G. Liang, H. Hong, W. Xie, and L. Zheng, “Combining convolutional neural network with recursive neural network for blood cell image classification,” IEEE Access, vol. 6, pp. 36188–36197, 2018.

T. Nakazawa and D. V. Kulkarni, “Wafer map defect pattern classification and image retrieval using convolutional neural network,” IEEE Transactions on Semiconductor Manufacturing, vol. 31, no. 2, pp. 309–314, May 2018.

P. Zhang, X. Wang, W. Zhang, and J. Chen, “Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 1, pp. 31–42, Jan. 2019.

S. Akcay, M. E. Kundegorski, C. G. Willcocks, and T. P. Breckon, “Using deep convolutional neural network architectures for object classification and detection within X-ray baggage security imagery,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 9, pp. 2203–2215, Sep. 2018.

K. Kavukcuoglu, M. Ranzato and Y. LeCun, Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition, New York University, New York, 2008.

C. C. Aggarwal, Neural Networks and Deep Learning: A Textbook, Berlin: Springer, 2018.

A. Zhang, Z. C. Lipton, M. Li and A. J. Smola, Dive into deep learning, 2019. [Online]. Available at:

M. Tanaka, T. Isokawa, N. Matsui, T. Yumoto, and N. Kamiura, “A convolutional autoencoder for detecting tumors in double contrast X-ray images,” Proceedings of the 2018 Joint 7th International Conference on Informatics, Electronics Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision Pattern Recognition (icIVPR), 2018, pp. 384–387.

Z. Yu et al., ‘A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 3, pp. 874–885, May 2018.

Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan, “Chest pathology detection using deep learning with non-medical training,” Proceedings of the 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015, pp. 294–297.

D. Bardou, K. Zhang, and S. M. Ahmad, “Classification of breast cancer based on histology images using convolutional neural networks,” IEEE Access, vol. 6, pp. 24680–24693, 2018.

G. Yang et al., “Automatic segmentation of kidney and renal tumor in CT images based on 3D fully convolutional neural network with pyramid pooling module,” Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), 2018, pp. 3790–3795.

S. Wang, Y. Shen, D. Zeng, and Y. Hu, “Bone age assessment using convolutional neural networks,” Proceedings of the 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), 2018, pp. 175–178.

J. Rathod, V. Waghmode, A. Sodha and P. Bhavathankar, “Diagnosis of skin diseases using convolutional neural networks,” Proceedings of the 2nd Int. Conf. on Electronics, Communication, and Aerospace Technology (ICECA 2018), 2018, pp. 1048-1051.

C. Chin, M. Chin, T. Tsai, and W. Chen, “Facial skin image classification system using convolutional neural networks deep learning algorithm,” Proceedings of the 2018 9th International Conference on Awareness Science and Technology (iCAST), 2018, pp. 51–55.

H. S. Baweja and T. Parhar, “Leprosy lesion recognition using convolutional neural networks,” Proceedings of the 2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016, vol. 1, pp. 141–145.

P. Moeskops, M. A. Viergever, A. M. Mendrik, L. S. de Vries, M. J. N. L. Benders, and I. Išgum, “Automatic segmentation of MR brain images with a convolutional neural network,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1252–1261, May 2016.

N. Tajbakhsh et al., ‘Convolutional neural networks for medical image analysis: Full training or fine tuning?” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1299–1312, May 2016.

M. J. J. P. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1273–1284, May 2016.

H. Salehinejad, E. Colak, T. Dowdell, J. Barfett, and S. Valaee, “Synthesizing chest X-ray pathology for training deep convolutional neural networks,” IEEE Transactions on Medical Imaging, vol. 38, no. 5, pp. 1197–1206, May 2019.

Z. Liu, C. Cao, S. Ding, Z. Liu, T. Han, and S. Liu, ‘Towards clinical diagnosis: Automated stroke lesion segmentation on multi-spectral MR image using convolutional neural network,” IEEE Access, vol. 6, pp. 57006–57016, 2018.

X. Xu, J. Lin, Y. Tao, and X. Wang, “An improved DenseNet method based on transfer learning for fundus medical images,” Proceedings of the 2018 7th International Conference on Digital Home (ICDH), 2018, pp. 137–140.

G. Yang, U. B. Gewali, E. Ientilucci, M. Gartley, and S. T. Monteiro, ‘Dual-channel DenseNet for hyperspectral image classification,” Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium IGARSS’2018, 2018, pp. 2595–2598.

K. Zhang, Y. Guo, X. Wang, J. Yuan, and Q. Ding, ‘Multiple feature reweight DenseNet for image classification,” IEEE Access, vol. 7, pp. 9872–9880, 2019.

S. Suzuki et al., “Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis,” Proceedings of the 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 2016, pp. 1382–1386.

Y. F. Riti, H. A. Nugroho, S. Wibirama, B. Windarta, and L. Choridah, “Feature extraction for lesion margin characteristic classification from CT Scan lungs image,” Proceedings of the 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2016, pp. 54–58.

R. Kumar, R. Srivastava, and S. Srivastava, “Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features,” Journal of Medical Engineering, vol. 2015, 2015, pp. 1–14.

R. Zahedinasab and H. Mohseni, “Using deep convolutional neural networks with adaptive activation functions for medical CT brain image classification,” Proceedings of the 2018 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME), 2018, pp. 1–6.

J. Xu, Y. Chae, B. Stenger, and A. Datta, “Dense Bynet: Residual dense network for image super resolution,” Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), 2018, pp. 71–75.

M. Nielsen, Neural Networks and Deep Learning. [Online]. Available at:

C.-Y. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,” Journal of Machine Learning Research, vol. 38, pp. 562–570, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9908, LNCS, pp. 630–645, 2016.

Z. Luo, L. Liu, J. Yin, Y. Li, and Z. Wu, “Latent ability model: A generative probabilistic learning framework for workforce analytics,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 5, pp. 923–937, May 2019.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, ‘Rethinking the inception architecture for computer vision,” Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826.




How to Cite

Mienye, I. D., Sun, Y., & Wang, Z. (2020). IMPROVED PREDICTIVE SPARSE DECOMPOSITION METHOD WITH DENSENET FOR PREDICTION OF LUNG CANCER. International Journal of Computing, 19(4), 533-541.