Volume 18 , Issue 2 , PP: 174-185, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed I. Alghamdi 1 *
Doi: https://doi.org/10.54216/IJNS.180202
Lung cancer is the abnormal development of cells in the lung causes serious risk to the health since lung has an interconnected system of blood vessel and lymphatic channel exposed to metastasis. The survival rate of lung cancer depends greatly on the earlier diagnosis and staging of the lung cancer. Computed Tomography (CT) image is commonly employed for lung cancer diagnosis since they offer data regarding distinct portions of the lung. The exactness of finding tumor location, volume and shape acting a major role in positive treatment and diagnosis of tumor. This article designs a novel neutrosophic set with adaptive neuro-fuzzy inference system for liver tumor segmentation and classification (NSANFIS-LTSC) model. The presented NSANFIS-LTSC model aims to identify and classify the presence of liver tumor from medical images. The presented NSANFIS-LTSC model primarily undergoes pre-processing to eradicate the noise. Followed by, the neutrosophic set (NS) based segmentation is applied to identify the affected tumor regions in the CT images. Besides, DenseNet-169 model is utilized to create feature vectors and dragonfly algorithm (DFA) is applied to tune the hyper parameters of the DenseNet-169 model. Finally, ANFIS classifier is exploited for the occurrence and classification of liver tumor. The simulation analysis of the NSANFIS-LTSC model is experimented using benchmark dataset and the results are investigated under several aspects. The simulation outcome reported the betterment of the NSANFIS-LTSC model over the recent methodologies.
Liver tumor, Image segmentation, Medical imaging, Deep learning, Disease classification ,
[1] Almotairi, S., Kareem, G., Aouf, M., Almutairi, B. and Salem, M.A.M., 2020. Liver tumor segmentation in CT scans using modified SegNet. Sensors, 20(5), p.1516.
[2] Rela, M., Suryakari, N.R. and Reddy, P.R., 2020, September. Liver tumor segmentation and classification: a systematic review. In 2020 IEEE-HYDCON (pp. 1-6). IEEE.
[3] Rezaei, M., Yang, H. and Meinel, C., 2018, July. Instance tumor segmentation using multitask convolutional neural network. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
[4] Anter, A.M. and Hassenian, A.E., 2019. CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm. Artificial intelligence in medicine, 97, pp.105-117.
[5] Rao, C.S. and Karunakara, K., 2021. A comprehensive review on brain tumor segmentation and classification of MRI images. Multimedia Tools and Applications, 80(12), pp.17611-17643.
[6] Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W. and Heng, P.A., 2018. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE transactions on medical imaging, 37(12), pp.2663-2674.
[7] Rela, M., Nagaraja Rao, S. and Ramana Reddy, P., 2021. Optimized segmentation and classification for liver tumor segmentation and classification using opposition‐based spotted hyena optimization. International Journal of Imaging Systems and Technology, 31(2), pp.627-656.
[8] Zhang, D., Chen, B., Chong, J. and Li, S., 2021. Weakly-supervised teacher-student network for liver tumor segmentation from non-enhanced images. Medical Image Analysis, 70, p.102005.
[9] Jiang, H., Diao, Z. and Yao, Y.D., 2021. Deep learning techniques for tumor segmentation: a review. The Journal of Supercomputing, pp.1-45.
[10] Anil, B.C. and Dayananda, P., 2021. Automatic liver tumor segmentation based on multi-level deep convolutional networks and fractal residual network. IETE Journal of Research, pp.1-9.
[11] Xu, Y., Cai, M., Lin, L., Zhang, Y., Hu, H., Peng, Z., Zhang, Q., Chen, Q., Mao, X., Iwamoto, Y. and Han, X.H., 2021. PA‐ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images. Medical Physics, 48(7), pp.3752-3766.
[12] Chlebus, G., Schenk, A., Moltz, J.H., van Ginneken, B., Hahn, H.K. and Meine, H., 2018. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific reports, 8(1), pp.1-7.
[13] Deng, Z., Guo, Q. and Zhu, Z., 2019. Dynamic regulation of level set parameters using 3D convolutional neural network for liver tumor segmentation. Journal of healthcare engineering, 2019.
[14] Zheng, Z., Zhang, X., Xu, H., Liang, W., Zheng, S. and Shi, Y., 2018. A unified level set framework combining hybrid algorithms for liver and liver tumor segmentation in CT images. BioMed research international, 2018.
[15] Anter, A.M. and Hassenian, A.E., 2018. Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation. Journal of Computational Science, 25, pp.376-387.
[16] Zeng, M. and Xiao, N., 2019. Effective combination of DenseNet and BiLSTM for keyword spotting. IEEE Access, 7, pp.10767-10775.
[17] Mirjalili, S., 2016. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural computing and applications, 27(4), pp.1053-1073.
[18] Thirumurugan, P. and Shanthakumar, P., 2016. Brain tumor detection and diagnosis using ANFIS classifier. International Journal of Imaging Systems and Technology, 26(2), pp.157-162.
