Advertisement

Abstract

As the most fatal cancer type, early diagnosis of the lung cancer plays an important role for the survival of the patients. Diagnosis of the lung cancer involves screening the patients initially by Computed Tomography (CT) for the presence of lung lesions. This procedure requires expert radiologists which need to go over very large numbers of image slices manually in order to detect and diagnose lung lesions. Unfortunately this is a very time consuming process and its performance is very dependent on the performing radiologist. Thus assisting the radiologists by developing an automated computer aided detection (CAD) system is an interesting research goal. In this regard, as the aim of this paper a pre-trained AlexNet (deep learning) framework is transferred to develop and implement a robust CAD system for the classification of lung images depending on whether they bear a lung lesion or not. High performances of 98.72% sensitivity, 98.35% specificity and 98.48% accuracy are reported as a result.

Keywords

Deep learning Lung lesion detection Biomedical image processing Transfer learning 

References

  1. 1.
    Işın, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102, 317–324 (2016)CrossRefGoogle Scholar
  2. 2.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2016)Google Scholar
  3. 3.
    Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)CrossRefGoogle Scholar
  4. 4.
    Işın, A., Ozdalili, S.: Cardiac arrhythmia detection using deep learning. Procedia Comput. Sci. 120, 268–275 (2017)CrossRefGoogle Scholar
  5. 5.
    Reeves, A.P., et al.: A public image database to support research in computer aided diagnosis. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 3715–3718. IEEE, September 2009Google Scholar
  6. 6.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE, June 2009Google Scholar
  7. 7.
    Wang, G.: A perspective on deep learning. IEEE Access 4, 8914–8924 (2016)CrossRefGoogle Scholar
  8. 8.
    Işın, A., Ozsahin, D.U., Dutta, J., Haddani, S., El-Fakhri, G.: Monte carlo simulation of PET/MR scanner and assessment of motion correction strategies. J. Instrum. 12(03), C03089 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringNear East UniversityNicosiaTurkey
  2. 2.Department of Electrical EngineeringAjman UniversityAjmanUnited Arab Emirates

Personalised recommendations