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Improved Lung Nodule Classification using Multi-class Artificial Neural Network with Back Propagation Algorithm
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This paper designed a novel method to overcome the problem of the lung nodule overlapping adjacent structures. We developed a lobe segmentation algorithm for identifying lung lobes CT images. To find reliable method for nodule detection is an important problem in medicine. It requires efficient automatic method to perform segmentation and detection. The identification of tumor region involves extraction of lobar fissures from the input CT images which makes use of two phases. In the first phase the fracture region is identified. In the second phase the found fissure are extracted. There is some nodule-like object in testing data detected by algorithm and not included in ground truth information. These are probably nodules missed by human. We designed a novel method to overcome the problem of the lung nodule overlapping adjacent structures. The result Obtained show that the proposed work can help the surgeons to identify the lobar fissures correctly to locate the lung region before they plan for the surgery. It reduces the computation time and complexity. Our system was developed with Faculty Hospital, Motol and Prague and in future should be used there. In order to improve the performance of the proposed approaches some future enhancements could be necessary in the present research work.
Keywords
CT Image, Lung Nodule, Classification, ANN.
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- S.G. Armato, M.L. Giger and H. MacMaho, “Automated Detection of Lung Nodules in CT Scans: Preliminary Results”, Medical Physics, Vol. 28, No. 8, pp. 1552-1561, 2001.
- C. Bhuvaneswari, P. Aruna and D. Loganathan, “A Novel Shape Based Feature Extraction Technique For Diagnosis of Lung Diseases using Evolutionary Approach”, ICTACT Journal of Soft Computing, Vol. 4, No. 4, pp. 804-810, 2014.
- F. Zhang, Y. Song, W. Cai, M.Z. Lee, Y. Zhou, H. Huang, S. Shan, M.J. Fulham and D.D. Feng, “Lung Nodule Classification with Multilevel Patch-Based Context Analysis”, IEEE Transactions on Biomedical Engineering, Vol. 61, No. 4, pp. 1155-1166, 2014.
- H. Takizawa, Y. Shinji and S. Tsuyoshi, “Recognition of Pulmonary Nodules Thoracic CT Scans using 3D Deformable Object Models of Different Classes”, Algorithms, Vol. 3, No. 2, pp. 125-144, 2010.
- A. Farag, A. Ali, J. Graham, S. Elshazly and R. Falk, “Evaluation of Geometric Feature Descriptors for Detection and Classification of Lung Nodules in Low Dose CT Scans of the Chest”, Proceedings of International Symposium on Biomedical Imaging from Nano to Macro, pp. 169-172, 2011.
- Amal A. Farag, Shireen Y. Elhabian, Salwa A. Elshazly and Aly A. Farag, “Quantification of Nodule Detection in Chest CT: A Clinical Investigation Based on the ELCAP Study”, Proceedings of International Conference on Image Processing, pp. 149-160, 2010.
- Y. Lee, T. Hara, H. Fujita, S. Itoh and T. Ishigaki, “Automated Detection of Pulmonary Nodules in Helical CT Images based on an Improved Template Matching Technique”, IEEE Transactions on Medical Imaging, Vol. 20, No. 7, pp. 595-604, 2001.
- S.L.A. Lee, A.Z. Kouzani and E.J. Hu, “Automated Detection of Lung Nodules in Computed Tomography Images: A Review”, Machine Vision and Applications, Vol. 23, No. 1, pp. 151-163, 2012.
- Y. Song, W. Cai, Y. Zhou and D. Feng, “Feature based Image Patch Approximation for Lung Tissue Classification”, IEEE Transactions on Medical Imaging, Vol. 32, No. 4, pp. 797-808, 2013.
- L. Srensen, S.B. Shaker and M. DeBurjin, “Quantitative Analysis of Pulmonary Emphysema using Local Binary Pattern”, IEEE Transactions on Medical Imaging, Vol. 29, No. 2, pp. 559-569, 2010.
- X. Ujiong, Y. Xinjulin, J. Dehmesk, G. Slabaugh and G. Beddoe, “Shape based Computer-Aided Detection of Lung Nodules in Thoracic CT Images”, IEEE Transactions on Biomedical Engineering, Vol. 56, No. 7, pp. 1810-1820, 2009.
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