Computer-Aided Detection of Lung Nodules in Chest X-Rays using Deep Convolutional Neural Networks

Chest X-Rays are most accessible medical imaging technique for diagnosing abnormalities in the heart and lung area. Automatically detecting these abnormalities with high accuracy could greatly enhance real world diagnosis processes. In this study we aim to improve the accuracy of convolutional deep learning by using Laplacian of Gaussian filtering. In this study, we have used the publicly available Japanese Society of Radiological Technology dataset including 247 radiograms. For improving the performance of convolutional neural networks we used LoG filter and also we used an advanced version of AlexNet and GoogleNet to compare our results. The results indicated that, convolutional neural network with Laplacian of Gaussian filter model produced the best results with 82.43% accuracy. Convolutional neural network with Laplacian of Gaussian filter model is followed by convolutional neural network with an accuracy of 72.97%, followed by GoogleNet model with an accuracy of 68.92%. Out of the four model types utilized, the AlexNet model produced the lowest accuracy with a value of 64.86%. The results obtained here demonstrate that the pre-processing technique like Laplacian of Gaussian filter can improve the accuracy.

Akciğer Nodüllerinin Göğüs Röntgenlerinden Derin Evrişimsel Sinir Ağları Kullanılarak Bilgisayar Destekli Tespiti

Göğüs röntgenleri, kalp ve akciğerlerdeki anormallikleri teşhis etmek için en kolay erişilebilir tıbbi görüntüleme tekniğidir. Bu anormallikleri otomatik olarak yüksek hassasiyetle tespit etmek gerçek hayattaki teşhis süreçlerini büyük ölçüde artırmaktadır. Bu çalışmada, Gauss Laplace filtresini (LoG) kullanarak evrişimsel derin öğrenmenin doğruluk değerini arttırmayı amaçladık. Çalışmada, kamuya açık bir şekilde sunulan Japon Radyoloji Teknolojileri Derneğine ait 247 göğüs röntgeni görüntüsü kullanılmıştır. Evrişimsel sinir ağlarının performansını arttırmak için LoG filtresini ve daha sonra sonuçlarımızı karşılaştırmak için AlexNet ve GoogleNet modellerinin gelişmiş bir versiyonunu kullandık. Sonuçlar Gauss Laplace filtre modeli kullanılmış evrişimsel sinir ağının % 82.43 doğrulukla en iyi sonuçları verdiğini göstermiştir. Bu modeli, % 72.97 doğrulukla evrişimsel sinir ağı, % 68.92 doğrulukla GoogleNet modeli izlemektedir. Kullanılan dört model türünden AlexNet modeli, % 64.86 değeri ile en düşük doğruluğu üretmiştir. Burada elde edilen sonuçlar, görüntü ön işleme tekniklerinden Gauss Laplace filtresinin doğruluğu artırabileceğini göstermektedir.

PDF

___

[1] A. Krizhevsky, I. Suskever and G.E. Hinton, "ImageNet classification with deep convolutional neural networks, " in International Conference on Neural Information Processing Systems, 2012, pp. 1106-1114.

[2] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition, " In ICLR, 2015.

[3] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, "Going deeper with convolutions, " in IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9.

[4] K. He, X. Zhang, S. Ren and J. Sun, "Deep residual learning for image recognition, " in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.

[5] M. Mostajabi, P. Yadollahpour and G. Shakhnarovich, "Feedforward semantic segmentation with zoom-out features, " in IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3376- 3385.

[6] H. Noh, S. Hong and B. Han, "Learning deconvolution network for semantic segmentation, " in Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015, pp. 1520-1528.

[7] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, "Semantic image segmentation with deep convolutional nets and fully connected CRFs, " Computer Science, vol. 4, pp. 357-361, 2016.

[8] B.V. Ginneken, A. A. A. Setio, C. Jacobs and F. Ciompi, "Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans, in IEEE International Symposium on Biomedical Imaging, 2015, pp. 286-289.

[9] L. Rongjian, Z. Wenlu, S. Heung-Il, W. Li, L. Jiang, S. Dinggang and J. Shuiwang, "Deep learning based imaging data completion for improved brain disease diagnosis, " in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014, pp. 305-312.

[10] H. R. Roth, L. Lu, J. Liu, J. Yao, A. Seff, K. Cherry, L. Kim and R.M. Summers, "Improving computer-aided detection using convolutional neural networks and random view aggregation, " IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1170-1181, 2016.

[11] Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen and H.Greenspan, "Chest pathology detection using deep learning with non-medical training, " in IEEE International Symposium on Biomedical Imaging, 2015, pp. 294-297.

[12] H. C. Shin, K. Roberts, L. Lu, D. Demnerfushman, J. Yao and R. M. Summers, "Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation, " in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2497-2506.

[13] Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition, " Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.

[14] O. Ronneberger, P. Fischer and T. Brox, U-net: "Convolutional networks for biomedical image segmentation, " in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, pp. 234-241.

[15] F. Milletari, N. Navab and S. A. Ahmadi, "V-net: Fully convolutional neural networks for volumetric medical image segmentation, " in 2016 Fourth International Conference on 3D Vision, 2016, pp. 565-571.

[16] J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation, " in IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431-3440.

[17] A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S. M. Swetter, H.M. Blau and S. Thrun, "Dermatologistlevel classification of skin cancer with deep neural networks, " Nature, vol. 542, no. 7639, pp. 115-118, 2017.

[18] V. Gulshan, L. Peng, M. Coram, M.C. Stumpe, D.Wu, A. Narayanaswamy, S. Venugopalan, K.Widner, T. Madams, J Cuadros, et al., "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, " Jama, vol. 316, no.22, pp. 2402-2410, 2016.

[19] P. Lakhani and S. Baskaran, "Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks, " Radiology, vol. 284, no.2, pp. 574- 582, 2017.

[20] P. Huang, S. Park, R. Yan, J. Lee, L.C. Chu, C.T. Lin, A. Hussien, J. Rathmell, B. Thomas, C.Chen, et al., "Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: A matched case-control study, " Radiology, vol. 286, no.2, pp. 286-295, 2017.

[21] P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K.Shpanskaya, M. P. Lungren and Y. Ng. Andrew, "CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning, " in IEEE Conference on Computer Vision and Pattern Recognition, 2017.

[22] Y. Gordienko, Y. Kochura, O. Alienin, O. Rokovyi, S. Stirenko, P. Gang, J. Hui and W. Zeng, "Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer," in International Conference on Advanced Computational Intelligence, 2018.

[23] J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K. Komatsu, M. Matsui, H. Fujita, Y. Kodera and K. Doi, "Development of a digital image database for chest radiographs with and without a lung nodule, " AJR Am J Roentgenol, vol.174, no.1, pp. 71-74, 2000.

[24] G. E. Sotak, Jr. and K. L. Boyer, "The Laplacian-of-Gaussian kernel: a formal analysis and design procedure for fast, accurate convolution and full-frame output, " Comput.Vis.Gr. Image Process, vol. 48, no. 2, pp. 147-189, 1989.

[25] A. Huertas and G. Medioni, "Detection of intensity changes with subpixel accuracy using Laplacian–Gaussian masks, " IEEE Trans. Pattern Anal. Mach. Intell, vol. 8, no.5, pp. 651-664, 1986.

[26] M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, "Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, " IEEE Trans. Med. Imaging, vol. 35, no.5, pp. 1207-1216, 2016.

[27] M. Çoşkun, Ö. Yıldırım, A. Uçar, and Y. Demir, "An overview of popular deep learning methods," European Journal of Technique, vol. 7, no. 2, pp. 165-176, 2017.

[28] H.C. Shin, H.R Roth, M. Gao, et al., "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning," IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285-1298, 2016.