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Deep learning-based soft computing model for image classification application

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Abstract

The growth of swarm intelligence approaches and machine learning models in the field of medical image processing is extravagant, and the applicability of these approaches for various types of cancer classification has as well grown in the recent years. Considering the growth of these machine learning models, in this work attempt is taken to develop an optimized deep learning neural network classifier for classifying the nodule tissues in the lung cancer images which is an important application in biomedical area. The optimized model developed is the hybrid version of adaptive multi-swarm particle swarm optimizer with the new improved firefly algorithm resulting in better exploration and exploitation mechanism to determine near-optimal solutions. Multi-swarm particle swarm optimizer (MSPSO) possesses strong exploration capability due to its regrouping schedule nature, and the improved firefly algorithm (ImFFA) possesses better exploitation mechanism due to its inherit attractiveness and intensity feature. At this juncture, the new adaptive MSPSO–ImFFA is applied to the deep learning neural classifier to overcome the local and global minima occurrences and premature convergence by tuning its weight values. As a result, in this work the new adaptive MSPSO–ImFFA-based deep learning neural network classifier is employed to classify the lung cancer tissues of the considered lung computed tomography images. Results obtained prove the effectiveness of the deep learning classifier for the considered lung image sample datasets in comparison with the other methods compared from the previous literature works.

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References

  • ALzubi JA, Bharathikannan B, Tanwar S, Manikandan R, Khanna A, Thaventhiran C (2019) Boosted neural network ensemble classification for lung cancer disease diagnosis. Appl Soft Comput 80:579–591

    Google Scholar 

  • Antonio VAA, Ono N, Saito A, Sato T, Altaf-Ul-Amin M, Kanaya S (2018) Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks. Int J Comput Assist Radiol Surg 13(12):1905–1913

    Google Scholar 

  • Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP (2019) End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 25(6):954

    Google Scholar 

  • Asuntha NS, Srinivasan A (2016) PSO, genetic optimization and SVM algorithm used for lung cancer detection. J Chem Pharm Res 8(6):351–359

    Google Scholar 

  • Ayyildiz O, Aydin Z, Yilmaz B, Karaçavuş S, Şenkaya K, Icer S, Taşdemir EA, Kaya E (2020) Lung cancer subtype differentiation from positron emission tomography images. Turk J Electr Eng Comput Sci 28(1):262–274

    Google Scholar 

  • Bhuvaneswari P, Brinthaerese A (2014) Detection of cancer in lung with K-NN classification using genetic algorithm. Int Conf Nanomater Technol 10:433–440

    Google Scholar 

  • Blackwell T (2005) Particle swarms and population diversity. Soft Comput 9:793–802

    MATH  Google Scholar 

  • Blackwell TM, Bentley PJ (2002) Dynamic search with charged swarms. In: Proceedings of the genetic and evolutionary computation conference, New York, NY, USA, 9–13 July 2002, pp 19–26

  • Chen CH, Lee YW, Huang YS, Lan WR, Chang RF, Tu CY, Chen CY, Liao WC (2019) Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network. Comput Methods Programs Biomed 177:175–182

    Google Scholar 

  • de Carvalho Filho AO, Silva AC, de Paiva AC, Nunes RA, Gattass M (2018) Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network. Pattern Recogn 81:200–212

    Google Scholar 

  • Han G, Liu X, Zhang H, Zheng G, Soomro NQ, Wang M, Liu W (2019) Hybrid resampling and multi-feature fusion for automatic recognition of cavity imaging sign in lung CT. Fut Gener Comput Syst 99:558–570

    Google Scholar 

  • Higashi N, Iba H (2003) Particle swarm optimization with Gaussian mutation. In: Proceedings of the 2003 IEEE swarm intelligence symposium (SIS’03), Indianapolis, IN, USA, 26 April 2003, pp 72–79

  • Huidrom R, Chanu YJ, Singh KM (2019) Pulmonary nodule detection on computed tomography using neuro-evolutionary scheme. SIViP 13(1):53–60

    Google Scholar 

  • Hussein S, Kandel P, Bolan CW, Wallace MB, Bagci U (2019) Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging 38(8):1777–1787

    Google Scholar 

  • Jakimovski G, Davcev D (2019) Using double convolution neural network for lung cancer stage detection. Appl Sci 9(3):427

    Google Scholar 

  • Jung H, Kim B, Lee I, Lee J, Kang J (2018) Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method. BMC Med Imaging 18(1):48

    Google Scholar 

  • Kavitha MS, Shanthini J, Sabitha R (2019) ECM-CSD: an efficient classification model for cancer stage diagnosis in CT lung images using FCM and SVM techniques. J Med Syst 43(3):73

    Google Scholar 

  • Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113:202–209

    Google Scholar 

  • Lakshmanaprabu SK, Mohanty SN, Shankar K, Arunkumar N, Ramirez G (2019) Optimal deep learning model for classification of lung cancer on CT images. Fut Gener Comput Syst 92:374–382

    Google Scholar 

  • Ling SH, Iu HHC, Chan KY, Lam HK, Yeung BCW, Leung FH (2008) Hybrid particle swarm optimization with wavelet mutation and its industrial applications. IEEE Trans Syst Man Cybern B Cybern 38:743–763

    Google Scholar 

  • Lovbjerg M, Krink T (2002) Extending particle swarm optimisers with self-organized criticality. In: Proceedings of the 2002 congress on evolutionary computation (CEC’02), Honolulu, HI, USA, 12–17 May 2002, vol 2, pp 1588–1593

  • Manickavasagam R, Selvan S (2019) Automatic detection and classification of lung nodules in CT image using optimized neuro fuzzy classifier with cuckoo search algorithm. J Med Syst 43(3):77

    Google Scholar 

  • Matsubara T, Ochiai T, Hayashida M, Akutsu T, Nacher J (2018) Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles. In: 2018 IEEE 18th international conference on bioinformatics and bioengineering (BIBE). IEEE, pp 151–154

  • Nishio M, Sugiyama O, Yakami M, Ueno S, Kubo T, Kuroda T, Togashi K (2018) Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PLoS ONE 13(7):e0200721

    Google Scholar 

  • Paing MP, Hamamoto K, Tungjitkusolmun S, Pintavirooj C (2019) Automatic detection and staging of lung tumors using locational features and double-staged classifications. Appl Sci 9(11):2329

    Google Scholar 

  • Palani D, Venkatalakshmi K (2019) An IoT based predictive modelling for predicting lung cancer using fuzzy cluster based segmentation and classification. J Med Syst 43(2):21

    Google Scholar 

  • Pang S, Zhang Y, Ding M, Wang X, Xie X (2020) A deep model for lung cancer type identification by densely connected convolutional networks and adaptive boosting. IEEE Access 8:4799–4805

    Google Scholar 

  • Sahu P, Yu D, Dasari M, Hou F, Qin H (2018) A lightweight multi-section CNN for lung nodule classification and malignancy estimation. IEEE J Biomed Health Inform 23(3):960–968

    Google Scholar 

  • Šarić M, Russo M, Stella M, Sikora M (2019) CNN-based method for lung cancer detection in whole slide histopathology images. In: 2019 4th international conference on smart and sustainable technologies (SpliTech). IEEE, pp 1–4

  • Shakeel PM, Burhanuddin MA, Desa MI (2019a) Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks. Measurement 145:702–712

    Google Scholar 

  • Shakeel PM, Desa MI, Burhanuddin MA (2019b) Improved watershed histogram thresholding with probabilistic neural networks for lung cancer diagnosis for CBMIR systems. Multimed Tools Appl 1:1–19

    Google Scholar 

  • Shen S, Han SX, Aberle DR, Bui AA, Hsu W (2019) An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert Syst Appl 128:84–95

    Google Scholar 

  • Taher F, Werghi N, Al-Ahmad H, Sammouda R (2012) Lung cancer detection by using artificial neural network and fuzzy clustering methods. Am J Biomedl Eng 2(3):136–142

    Google Scholar 

  • Teramoto A, Tsukamoto T, Yamada A, Kiriyama Y, Imaizumi K, Saito K, Fujita H (2020a) Deep learning approach to classification of lung cytological images: two-step training using actual and synthesized images by progressive growing of generative adversarial networks. PLoS ONE 15(3):e0229951

    Google Scholar 

  • Teramoto A, Yamada A, Tsukamoto T, Imaizumi K, Toyama H, Saito K, Fujita H (2020) Decision support system for lung cancer using PET/CT and microscopic images. In: Qiu Y (ed) Deep learning in medical image analysis. Springer, Cham, pp 73–94

    Google Scholar 

  • Tunc T (2012) A new hybrid method logistic regression and feed forward neural network for lung cancer data. Math Probl Eng 2012:1–10

    Google Scholar 

  • Uthoff J, Stephens MJ, Newell JD Jr, Hoffman EA, Larson J, Koehn N, De Stefano FA, Lusk CM, Wenzlaff AS, Watza D, Neslund-Dudas C (2019) Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Med Phys 46(7):3207–3216

    Google Scholar 

  • Wang H, Sun H, Li C, Rahnamayan S, Pan JS (2013) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135

    MathSciNet  Google Scholar 

  • Wang C, Tyagi N, Rimner A, Hu YC, Veeraraghavan H, Li G, Hunt M, Mageras G, Zhang P (2019) Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network. Radiother Oncol 131:101–107

    Google Scholar 

  • Wang B, Si S, Cui E, Zhao H, Yang D, Dou S, Zhu J (2020) A fast and efficient CAD system for improving the performance of malignancy level classification on lung nodules. IEEE Access 8:40151–40170

    Google Scholar 

  • Woźniak M, Połap D, Capizzi G, Sciuto GL, Kośmider L, Frankiewicz K (2018) Small lung nodules detection based on local variance analysis and probabilistic neural network. Comput Methods Programs Biomed 161:173–180

    Google Scholar 

  • Xiao Z, Du N, Geng L, Zhang F, Wu J, Liu Y (2019) Multi-scale heterogeneous 3D CNN for false-positive reduction in pulmonary nodule detection, based on chest CT images. Appl Sci 9(16):3261

    Google Scholar 

  • Xie Y, Zhang J, Xia Y (2019) Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT. Med Image Anal 57:237–248

    Google Scholar 

  • Zhan ZH, Zhang J, Li Y, Chung HSH (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B Cybern 39:1362–1381

    Google Scholar 

  • Zhang G, Yang Z, Gong L, Jiang S, Wang L (2019a) Classification of benign and malignant lung nodules from CT images based on hybrid features. Phys Med Biol 64(12):125011

    Google Scholar 

  • Zhang C, Sun X, Dang K, Li K, Guo XW, Chang J, Yu ZQ, Huang FY, Wu YS, Liang Z, Liu ZY (2019b) Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. The Oncologist 24(9):1159–1165

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, 641 032, India

    M. Revathi & I. Jasmine Selvakumari Jeya

  2. Department of Electrical and Electronics Engineering, Anna University, Regional Campus: Coimbatore, Coimbatore, Tamil Nadu, 641 046, India

    S. N. Deepa

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  1. M. Revathi
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  2. I. Jasmine Selvakumari Jeya
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  3. S. N. Deepa
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Correspondence to M. Revathi.

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Revathi, M., Jeya, I.J.S. & Deepa, S.N. Deep learning-based soft computing model for image classification application. Soft Comput 24, 18411–18430 (2020). https://doi.org/10.1007/s00500-020-05048-7

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