Abstract
In this book chapter, the authors aim to deliver in-depth details about the applications of deep learning and swarm intelligent algorithms for image-based cancer recognition and diagnosis. In this study, we first describe the overview of popular architectures of deep learning and swarm intelligent algorithms used for cancer recognition. In deep learning, we talk about convolutional neural networks, fully connected convolutional networks, and auto-encoders. In swarm intelligent algorithms, we talk about architecture of genetic algorithms. Secondly, this study presents a brief survey about the research exploiting deep learning and swarm intelligent algorithms for cancer recognition.
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References
Ramasubramaniam, K., Singh, A.: Machine learning using R. second ed. Acta Press (2018)
Haenssle, H.A., Fink, C., Schneiderbauera, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A.B.H., Thomas, L., Enk, A., Uhlmann, L.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 derma- tologists. Ann. Oncol. 1836–42 (2018). https://doi.org/10.1093/annonc/mdy166
Dorj, U.-O., Lee, K.-K., Choi, J.-Y., Lee1, M.: The skin cancer classification using deep convolutional neural network. Multimedia Tools and Applica- tions pp. 9909–24 (2018). https://doi.org/10.1007/s11042-018-5714-1
Marks, R.: Epidemiology of melanoma, clinical and experimental dermatology. Clin. Dermatol. 459–63 (2000). https://doi.org/10.1046/j.1365-2230.2000.00693.x
Fass, L.: Imaging and cancer: a review. Mol. Oncol. 2, 115–152 (2008)
Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31, 198–211 (2007)
IrfanView. [link]. https://www.irfanview.net/
Tushar Bhardwaj, Sharma, S.C.: An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach, Soft Computing (2018)
Tushar Bhardwaj, S.C. Sharma.: Fuzzy logic-based elasticity controller for autonomic resource provisioning in parallel scientific applications: a cloud computing perspective. Comput. Elect. Eng. 70, 1049–1073 (2018)
Tushar Bhardwaj, S.C.Sharma.: Cloud-WBAN: an experimental framework for cloud-enabled wireless body area network with efficient virtual resource utilization. Sustain. Comput. Inform. Syst. 20, 14–33 (2018)
Bhanot, K., Peddoju, S.K., Tushar Bhardwaj.: A model to find optimal percentage of training and testing data for efficient ECG analysis using neural network. Int. J. Syst. Assur. Eng. Manag. (2015). doi: https://doi.org/10.1007/s13198-015-0398-7
Yin, F.F., Giger, M.L., Vyborny, C.J., Schmidt, R.A.: Computerized detection of masses in digital mammograms: automated alignment of breast images and its effect on bilateral-subtraction technique. Med. Phys. 21, 445–452 (1994)
Beller, M., Stotzka, R., Müller, T., Gemmeke, H.: An example-based system to support the segmentation of stellate lesions. Bildverarb. Med. 2005, 475–479 (2005)
te Brake, G.M., Karssemeijer, N., Hendriks, J.H.: An automatic method to discriminate malignant masses from normal tissue in digital mammograms1. Phys. Med. Biol. 45, 2843 (2000)
Eltonsy, N.H., Tourassi, G.D., Elmaghraby, A.S.: A concentric morphology model for the detection of masses in mammography. IEEE Trans. Med. Imag. 26, 880–889 (2007)
Wei, J., Sahiner, B., Hadjiiski, L.M., Chan, H.P., Petrick, N., Helvie, M.A., Roubidoux, M.A., Ge, J., Zhou, C.: Computer-aided detection of breast masses on full field digital mammograms. Med. Phys. 32, 2827–2838 (2005)
Tushar Bhardwaj, Pandit, M.R., Sharma, T.K.: “A Safer Cloud”, Data Isolation and Security by Tus-Man Protocol. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28–30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi (2014)
Hawkins, S.H., Korecki, J.N., Balagurunathan, Y., Gu, Y., Kumar, V., Basu, S., Hall, L.O., Goldgof, D.B., Gatenby, R.A., Gillies, R.J.: Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access 2, 1418–1426 (2014)
Kavitha Kadarla, S.C. Sharma, Tushar Bhardwaj, Ajay Chaudhary.: A simulation study of response times in cloud environment for iot-based healthcare workloads. 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS-2017), vol. 00, pp. 678–683 (2017). doi:https://doi.org/10.1109/MASS.2017.65
Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Cavalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D.: Decoding tumour phe- notype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 1–8 4006 (2014)
Balagurunathan, Y., Gu, Y., Wang, H., Kumar, V., Grove, O., Hawkins, S., Kim, J., Goldgof, D.B., Hall, L.O., Gatenby, R.A.: Reproducibility and prognosis of quantitative features extracted from CT images, Transl. Oncol. 7, 72–87 (2014)
Tushar Bhardwaj, Himanshu Upadhyay, Subhash Chander Sharma.: Autonomic resource provisioning framework for service-based cloud applications: a queuing- model based approach. IEEE 10th International Conference on Cloud Computing, Data Science & Engineering (CONFLUENCE-2020). 29th−31st Jan 2020, India
Han, F., Wang, H., Zhang, G., Han, H., Song, B., Li, L., Moore, W., Lu, H., Zhao, H., Liang, Z.: Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J. Digit. Imaging 28, 99–115 (2015)
Pandit, M.R., Tushar Bhardwaj, Khatri, V.: Steps towards web ubiquitous computing. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28–30, 2012. Advances in Intelligent Systems and Computing, vol. 236. Springer, New Delhi (2014)
Barata, C., Marques, J.S., Celebi, M.E.: Improving dermoscopy image analysis using color constancy. Proceedings of 2014 IEEE International Conference on Image Processing (ICIP), pp. 3527–3531 (2014)
Barata, C., Marques, J.S., Rozeira, J.: A system for the detection of pigment network in dermoscopy images using directional filters. IEEE Trans. Biomed. Eng. 59, 2744–2754 (2012)
Tushar Bhardwaj, S.C.Sharma.: An efficient elasticity mechanism for server-based pervasive healthcare applications in cloud environment. 19th IEEE International Conference on High Performance Computing and Communications Workshops (HPCCWS 2017), Bangkok, Thailand
Bi, W.L., Hosny, A., Schabath, M.B., Giger, M.L., Birkbak, N.J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I.F., Mak, R.H.: Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J. Clinic. 69(2), 127–157 (2019)
Barata, C., Ruela, M., Mendonça, T., Marques, J.S.: A bag-of-features approach for the classification of melanomas in dermoscopy images: the role of color and texture descriptors. In Computer Vision Techniques For the Diagnosis of Skin Cancer, Springer, pp. 49–69 (2014)
Tushar Bhardwaj, Himanshu Upadhyay, Subhash Chander Sharma.: Framework for quality ranking of components in cloud computing: regressive rank. IEEE 10th International Conference on Cloud Computing, Data Science & Engineering (CONFLUENCE-2020). 29th−31st Jan 2020, India
Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.: Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans. Med. Imaging 32, 849–861 (2013)
Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Decision forests for tissue-specific seg- mentation of high-grade gliomas in multi-channel MR. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp. 369–376 (2012)
Tushar Bhardwaj, Himanshu Upadhyay, Subhash Chander Sharma.: Autonomic resource allocation mechanism for service-based cloud applications. IEEE International Conference on Computing, Communication, and Intelligent Systems (ICCCIS-2019). 18th−19th Oct 2019, India
Meier, R., Bauer, S., Slotboom, J., Wiest, R., Reyes, M.: A hybrid model for multi-modal brain tumor segmentation, Multimod. Brain Tumor Segm. 31 (2013)
Pinto, S., Pereira, H., Correia, J., Oliveira, D.M., Rasteiro, C.A.: Silva, Brain tumour segmentation based on extremely randomized forest with high-level features. Proceedings of the 37th Annual International Conference on IEEE Engineering in Medicine and Biology Society (EMBC, 2015), pp. 3037–3040
Tushar Bhardwaj, Himanshu Upadhyay, Subhash Chander Sharma.: An Autonomic Resource Allocation Framework for Service-based Cloud Applications: A Proactive Approach. 4th International Conference on Soft Computing: Theories and Applications (SoCTA - 2019). Advances in Intelligent Systems and Computing (AISC) Springer. Scopus Indexed. 27th−29th Dec 2019, India
Tustison, N.J., Shrinidhi, K., Wintermark, M., Durst, C.R., Kandel, B.M., Gee, J.C., Grossman, M.C., Avants, B.B.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13, 209–225 (2015)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–4 4 4 (2015)
Sharif, M., Amin, J., Raza, M., Yasmin, M., Satapathy, S.C.: An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recogn. Lett. 129, 150–157 (2020)
Chithambaram, T., Perumal, K.: Brain Tumor Segmentation using Genetic Algorithm and ANN Techniques. IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017) (2017)
Chakraborty Amrita, Kar, A.K.: Swarm Intelligence: A Review of Algorithms. In Nature-Inspired Computing and Optimization: Theory and Applications (ed. Patnaik Srikanta and Yang, X.-S. and N. K.) 475–494 (Springer International Publishing, 2017). doi:https://doi.org/10.1007/978-3-319-50920-4_19
Tushar Bhardwaj.: End-to-End Data Security for Multi-Tenant Cloud Environment. J. Comput. Technol. Applic. ISSN: 2229–6964, (2014)
Tushar Bhardwaj, Mohit Kumar, S.C.Sharma.: Megh: a private cloud provisioning various IaaS and SaaS. Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol. 584. Springer, Singapore (2016)
Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network (2015). arXiv preprint arXiv:1505.00853
Particle swarm optimization. en.wikipedia.org at https://en.wikipedia.org/wiki/Particle_swarm_optimization
Tushar Bhardwaj, Sharma T.K., Pandit M.R.: Social Engineering Prevention by Detecting Malicious URLs Using Artificial Bee Colony Algorithm. Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 258. Springer, New Delhi (2014)
Tarun Kumar Sharma, Millie Pant, Tushar Bhardwaj. PSO Ingrained Artificial Bee Colony Algorithm for Solving Continuous Optimization Problems. In Proceedings of IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE 2011), Malaysia, pp. 108–112
Introduction to Particle Swarm Optimization. mnemstudio.org. at http://www.mnemstudio.org/particle-swarm-introduction.htm
Genetic algorithm. en.wikipedia.org at https://en.wikipedia.org/wiki/Genetic_algorithm
Bridge, D.: Genetic Algorithms at http://www.cs.ucc.ie/~dgb/courses/tai/notes/handout12.pdf
Ant colony optimization algorithms. en.wikipedia.org at https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms
Tushar Bhardwaj, S.C.Sharma.: Internet of Things: route search optimization applying ant colony algorithm and theory of computation. Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol. 335. Springer, New Delhi (2015)
Binti Saidin, N. F. et al.: Using ant colony optimization (ACO) on kinetic modeling of the acetoin production in lactococcus lactis C7. Stud. Comput. Intell. 477, 25–35 (2013)
Saha, S.: A comprehensive guide to convolutional neural networks — the ELI5 way. Medium (2018). at https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic seg- mentation. Proceedings of the IEEE International Conference on Computer Vi- sion, 1520–1528 (2015)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In Proceedings of European Conference on Computer Vision, Springer, pp. 184–199 (2014)
Jain, V., Seung, S.: Natural image denoising with convolutional networks. Adv. Neural Inf. Process. Syst. 21, 769–776 (2009)
What is a fully convolution network?. Artificial Intelligence Stack Exchange. at https://ai.stackexchange.com/questions/21810/what-is-a-fully-convolution-network
Bishop, C.M.: Neural Networks For Pattern Recognition, Oxford University Press (1995)
Ng, Sparse autoencoder, CS294A Lect. Notes 72, 1–19 (2011)
Autoencoder Tutorial. edureka.co at https://www.edureka.co/blog/autoencoders-tutorial/
Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35, 1240–1251 (2016)
Gao, X.W., Hui, R., Tian, Z.: Classification of CT brain images based on deep learning networks. Comput. Methods Programs Biomed. 138, 49–56 (2017)
Kamble, T., Rane, P.: Brain Tumor segmentation using swarm intelligence approach. Intern. J. Sci. Eng. Res. 4 (2013)
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018)
Wang, L.: Early diagnosis of breast cancer. Sensors (Switzerland) 17, (2017)
Chen, H.L., et al.: Support vector machine based diagnostic system for breast cancer using swarm intelligence. J. Med. Syst. 36, 2505–2519 (2012)
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using Convolutional Neural Networks. 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, pp. 2560–2567 (2016). Doi: https://doi.org/10.1109/IJCNN.2016.7727519
Zamani, H., Nadimi-Shahraki, M.-H.: Swarm intelligence approach for breast cancer diagnosis. Intern. J. Comput. Applic. 151(1), 40–44 (2016). https://doi.org/10.5120/ijca2016911667
Machraoui, A.N., Cherni, M.A., Sayadi, M.: Ant colony optimization algorithm for breast cancer cells classification. In International Conference on Electrical Engineering and Software Applications, ICEESA 2013 (2013). doi:https://doi.org/10.1109/ICEESA.2013.6578445
Velusamy, P., Karantharaj, P., Prabakar, S.: New scheme for breast cancer detection and staging using ant colony algorithm. Int. J. Biomed. Eng. Technol. 27, 86 (2018)
Tan, T.Y., Zhang, L., Lim, C.P.: Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models. Appl. Soft Comput. J. 84 (2019)
Mahbod, R., Ecker, I.: Ellinger, skin lesion classification using hybrid deep neural networks. (2017). arXiv preprint arXiv:1702.08434
Sabouri, P., GholamHosseini, H.: Lesion border detection using deep learning. 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, pp. 1416−1421 (2016). doi: https://doi.org/10.1109/CEC.2016.7743955
Aswin, R.B., Jaleel, J.A., Salim, S.: Hybrid genetic algorithm—artificial neural network classifier for skin cancer detection. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, ICCICCT 2014 1304–1309 (Institute of Electrical and Electronics Engineers Inc., 2014). doi:https://doi.org/10.1109/ICCICCT.2014.6993162
Zghal, N.S., Kallel, I.K.: An effective approach for the diagnosis of melanoma using the sparse auto-encoder for features detection and the SVM for classification. In 2020 International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020 (Institute of Electrical and Electronics Engineers Inc., 2020). doi:https://doi.org/10.1109/ATSIP49331.2020.9231611
Asuntha, A., Singh, N., Srinivasan, A., Professor, A.: PSO, Genetic Optimization and SVM Algorithm used for Lung Cancer Detection. Available online www.jocpr.com J. Chem. Pharmac. Res. 8, 351–359 (2016)
Dou, Q., Chen, H., Yu, L., Qin, J., Heng, P.-A.: Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng. 64, 1558–1567 (2017)
Al-Hegami, A., Bin-Ghodel, A.: A particle swarm based approach for classification of cancer based on CT scan. Int. J. Comput. Applic. 178 (2019)
Hussein, S., Gillies, R., Cao, K., Song, Q., Bagci, U.: TumorNet: lung nodule characterization using multi-view convolutional neural network with gaussian process. In Proceedings of IEEE 14th International Symposium on Biomedical Imaging (ISBI), pp. 1007–1010 (2017)
Best, M.G., et al.: Swarm intelligence-enhanced detection of non-small-cell lung cancer using Tumor-educated platelets. Cancer Cell 32, 238-252.e9 (2017)
Thangavel, K., Manavalan, R.: Soft computing models based feature selection for TRUS prostate cancer image classification. Soft. Comput. 18, 1165–1176 (2014)
Abbasi, A.A., et al.: Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cogn. Neurodyn. 14, 523–533 (2020)
Hou, Q., et al.: RankProd combined with genetic algorithm optimized artificial neural network establishes a diagnostic and prognostic prediction model that revealed C1QTNF3 as a biomarker for prostate cancer. EBioMedicine 32, 234–244 (2018)
Wang, Y., Zheng, B., Gao, D., Wang, J.: Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation. 24th International Conference on Pattern Recognition (ICPR) (2018). doi:https://doi.org/10.0/Linux-x86_64
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Bhardwaj, T., Mittal, R., Upadhyay, H., Lagos, L. (2022). Applications of Swarm Intelligent and Deep Learning Algorithms for Image-Based Cancer Recognition. In: Garg, L., Basterrech, S., Banerjee, C., Sharma, T.K. (eds) Artificial Intelligence in Healthcare. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-16-6265-2_9
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