Medical image classification with hybrid convolutional neural network models

Öz

Despite important developments in medicine and technology today, many people die due to false or late diagnosis. It is very important to identify the small details in the images that can be overlooked in the examinations made on medical images in terms of early diagnosis of the disease. Therefore, it is vital in some cases to provide early diagnosis by detecting the details in the images automatically by computer systems. In the study carried out, it was aimed to diagnose the disease through medical images by classifying different types of images. For this purpose, convolutional neural networks, which are among deep learning techniques, were evaluated together with different classifier models. In the applied hybrid model approach, feature extraction was obtained from medical images with the convolutional neural network model. The extracted features are used to train different classification models. In the continuation of the study, the performance results obtained from the classifier models are compared. Two different datasets including brain MR images and lung x-ray images were used in the training and testing of hybrid models. In the study, images were classified into two categories as malignant and benign tumors in order to detect images containing malignant tumors in MR images. In order to identify images with pneumonia, the images are similarly classified into two categories, healthy and pneumonia. At the end of the study, the performance results obtained from the model approaches were compared and the performance evaluation of the models was performed.

Anahtar Kelimeler

• Deep learning,Medical diagnosis,Convolutional neural network,Data augmentation,Classification

Hibrit evrişimli sinir ağı modelleri ile tıbbi görüntü sınıflandırması

Öz

Günümüzde tıp ve teknolojideki önemli gelişmelere rağmen, birçok kişi yanlış veya geç tanı nedeniyle hayatını kaybetmektedir. Tıbbi görüntüler üzerinde yapılan muayenelerde hastalığın erken teşhisi açısından gözden kaçırılabilecek görüntülerdeki küçük detayların belirlenmesi çok önemlidir. Bu nedenle, bazı durumlarda görüntülerdeki detayları bilgisayar sistemleri tarafından otomatik olarak tespit ederek erken teşhis sağlamak hayati önem taşımaktadır. Yapılan çalışmada, farklı görüntü tiplerini sınıflandırarak hastalığın tıbbi görüntülerle teşhis edilmesi amaçlanmıştır. Bu amaçla, derin öğrenme teknikleri arasında yer alan evrişimli sinir ağları, farklı sınıflayıcı modellerle birlikte değerlendirilmiştir. Uygulanan hibrid model yaklaşımında, evrişimli sinir ağı modeli ile tıbbi görüntülerden özellik çıkarımı elde edilmiştir. Çıkarılan özellikler farklı sınıflandırma modellerini eğitmek için kullanılır. Çalışmanın devamında, sınıflandırıcı modellerinden elde edilen performans sonuçları karşılaştırılmıştır. Hibrid modellerin eğitim ve testinde beyin MR görüntüleri ve akciğer röntgeni görüntüleri dahil olmak üzere iki farklı veri seti kullanılmıştır. Çalışmada MR görüntülerinde malign tümör içeren görüntüleri saptamak için görüntüler malign ve benign tümörler olarak iki kategoriye ayrıldı. Akciğer iltihaplanmalı görüntüleri tanımlamak için görüntüler benzer şekilde sağlıklı ve akciğer iltihaplanması olmak üzere iki kategoriye ayrılır. Araştırma sonunda model yaklaşımlarından elde edilen performans sonuçları karşılaştırılmış ve modellerin performans değerlendirmesi yapılmıştır.

Anahtar Kelimeler

• Derin öğrenme,Tıbbi teşhis,Evrişimli sinir ağı,Veri artırma,Sınıflandırma

Kaynakça

1. Aghdam, H. H. and Heravi, E. J. (2017). Guide to Convolutional Neural Networks, NY: Springer, New York, USA.
2. Afshar, P., Mohammadi, A. and Plataniotis, K. N. (2018). “Brain tumor type classification via capsule networks.” Proc., 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, pp. 3129-3133. IEEE.
3. Bejnordi, B. E., Lin, J., Glass, B., Mullooly, M., Gierach, G. L., Sherman, M. E., Karssemeijer, N., van der Laak, J. and Beck, A. H. (2017). “Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images.” Proc., IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, pp. 929-932. IEEE.
4. Breiman, Leo. (2001). "Random forests." Machine Learning, Vol. 45, No. 1, pp. 5-32.
5. Chakrabarty, N. (2019). Brain MRI Images for Brain Tumor Detection.
6. Chen, X., Xu, Y., Wong, D. W. K., Wong, T. Y., and Liu, J. (2015). “Glaucoma detection based on deep convolutional neural network.” Proc., 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, pp. 715-718.
7. Chollet, F. (2017). “Xception: Deep learning with depthwise separable convolutions.” Proc., IEEE Conference on Computer Vision and Pattern Recognition, Hawaiʻi Convention Center, Honolulu, Hawaii, USA, pp. 1251-1258.
8. Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., and Greenspan, H. (2018). “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification.” Neurocomputing, Vol. 321, 321-331.

9. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning, MIT press, Massachusetts, USA.
10. He, K., Zhang, X., Ren, S. and Sun, J. (2016). “Deep residual learning for image recognition.” Proc., IEEE Conference On Computer Vision And Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770-778. IEEE.
11. Indraswari, R., Kurita, T., Arifin, A. Z., Suciati, N. and Astuti, E. R. (2019). “Multi-projection deep learning network for segmentation of 3D medical images.” Pattern Recognition Letters, Vol. 125, pp. 791-797.
12. Kermany, D. K. and Goldbaum, M. (2018). Labeled optical coherence tomography (OCT) and Chest X-Ray images for classification. Mendeley Data, 2.
13. Khobragade, S., Tiwari, A., Patil, C. Y. and Narke, V. (2016). “Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network.” Proc., IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, pp. 1-5. IEEE.
14. Korolev, S., Safiullin, A., Belyaev, M. and Dodonova, Y. (2017). “Residual and plain convolutional neural networks for 3D brain MRI classification.” Proc., IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, pp. 835-838. IEEE.
15. Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012). “Imagenet classification with deep convolutional neural networks.” Proc., Advences in Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, USA, pp. 1097-1105.
16. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D. (1989). “Backpropagation applied to handwritten zip code recognition.” Neural computation, Vol. 1, No. 4, pp. 541-551.
17. Martinez-Murcia, F. J., Ortiz, A., Gorriz, J. M., Ramirez, J. and Castillo-Barnes, D. (2019). “Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.” IEEE Journal of Biomedical and Health Informatics.
18. Mikołajczyk, A., and Grochowski, M. (2018). “Data augmentation for improving deep learning in image classification problem.” Proc., International Interdisciplinary PhD Workshop (IIPhDW), Szczecin, Poland, pp. 117-122.
19. Oh, S. L., Hagiwara, Y., Raghavendra, U., Yuvaraj, R., Arunkumar, N., Murugappan, M., and Acharya, U. R. (2018). “A deep learning approach for Parkinson’s disease diagnosis from EEG signals.” Neural Computing and Applications, 1-7.
20. Qian, Y., Bi, M., Tan, T., and Yu, K. (2016). “Very deep convolutional neural networks for noise robust speech recognition.” IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 24, No. 12, pp. 2263-2276.
21. Shahzadi, I., Tang, T. B., Meriadeau, F. and Quyyum, A. (2018). “CNN-LSTM: Cascaded framework for brain Tumour classification.” Proc., IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Sarawak, Malaysia, pp. 633-637. IEEE.
22. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014). “Dropout: a simple way to prevent neural networks from overfitting.” The Journal of Machine Learning Research, Vol. 15, No. 1, pp. 1929-1958.
23. URL-1: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection/metadata [Date of Accessed: 09.09.2019]
24. URL-2: Https://www.kaggle.com/ paultimothymooney/chestxray-pneumonia [Date of Accessed: 20.07.2019].
25. Wong, S. C., Gatt, A., Stamatescu, V., and McDonnell, M. D. (2016). “Understanding data augmentation for classification: when to warp?” Proc., International conference on digital image computing: techniques and applications (DICTA), IEEE, Canberra, Australia, pp.1-6.
26. Vapnik, V., and Chapelle, O. (2000). “Bounds on error expectation for support vector machines.” Neural Computation, Vol. 12, No. 9, pp. 2013-2036.
27. Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R. and Mittal, A. (2019). “Pneumonia detection using cnn based feature extraction.” Proc., IEEE Third International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, Tamil Nadu, India, pp. 1-7. IEEE.
28. Zhang, J., Xie, Y., Wu, Q. and Xia, Y. (2019). “Medical image classification using synergic deep learning.” Medical Image Analysis, Vol. 54, pp. 10-19