Abstract
The use of deep learning models has become widespread in different computer vision problems such as classification, detection, and segmentation. Many deep learning models have been developed in the segmentation of medical images. Although segmentation accuracy has been increased, segmentation performance needs to be improved due to the variability of tissue, cell and image acquisition methods. In the deep-learning-based segmentation and classification methods, the parameters of the method should be optimized in order to obtain more successful results for segmentation. In this study, the optimization of the parameters has been performed with five optimization algorithms according to segmentation loss. These algorithms are Grey Wolf Optimizer, Artificial Bee Colony (ABC), Genetic Algorithm, Particle Swarm Optimization (PSO), and Black Widow Optimization (BWO). In the experimental studies, each algorithm was run independently ten times and ABC obtained the lowest average segmentation loss with a value of 0.135. However, ABC achieved this performance about seven hours longer than PSO and about 5 h longer than BWO. Since the parameter optimization of CNN-based models takes much more time than other benchmarks, the convergence speed of algorithms is very important. For this reason, it has been observed that PSO is much more successful than other algorithms with an average run time of 9.438 h. As a result, considering the Jaccard similarity coefficient, it was seen that the model performance increased by 8.1% with the optimization compared to manual parameter selection.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
All data generated or analysed during this study are included in this published article (and its supplementary information files). The codes generated during the current study are not publicly available due to will use at my future study but are available from the corresponding author on reasonable request.
References
Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30:449–459
Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N (2016) Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 35:1313–1321
Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216
Bayramoglu N, Heikkilä J (2016) Transfer learning for cell nuclei classification in histopathology images. In: European conference on computer vision. Springer, pp 532–539
Bayramoglu N, Kannala J, Heikkilä J (2016) Deep learning for magnification independent breast cancer histopathology image classification. In: Pattern recognition (ICPR), 2016 23rd international conference on, 2016. IEEE, pp 2440–2445
Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305
Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203
Bochinski E, Senst T, Sikora T (2017) Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms. In: 2017 IEEE international conference on image processing (ICIP). IEEE, pp 3924–3928
Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22:61–79
Chen H, Dou Q, Wang X, Qin J, Heng P-A (2016) Mitosis detection in breast cancer histology images via deep cascaded networks. In: AAAI. pp 1160–1166
Cicerone MT, Camp CH (2018) Histological coherent Raman imaging: a prognostic review. Analyst 143:33–59
Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 411–418
Claesen M, De Moor B (2015) Hyperparameter search in machine learning. arXiv preprint arXiv:150202127
Cruz-Roa AA, Ovalle JEA, Madabhushi A, Osorio FAG (2013) A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 403–410
da Silva GL, da Silva Neto OP, Silva AC, de Paiva AC, Gattass M (2017) Lung nodules diagnosis based on evolutionary convolutional neural network. Multimed Tools Appl 76:19039–19055
Dufourq E, Bassett BA (2017) Eden: evolutionary deep networks for efficient machine learning. In: 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech). IEEE, pp 110–115
Gao Z, Wang L, Zhou L, Zhang J (2017) HEp-2 cell image classification with deep convolutional neural networks. IEEE J Biomed Health Inform 21:416–428
Gao F, Wu T, Li J, Zheng B, Ruan L, Shang D, Patel B (2018) SD-CNN: a shallow-deep CNN for improved breast cancer diagnosis. arXiv preprint arXiv:180300663
Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc Ser C (Appl Stat) 28:100–108
Hayyolalam V, Kazem AAP (2020) Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 87:103249
Holland JH (1992) Genetic algorithms. Sci Am 267:66–73
İnik Ö, Koç İ (2017) Gray wolf optimizer for knot placement in B-spline curve fitting. Gaziosmanpaşa Bilimsel Araşt Derg 6:97–109
İnik Ö, Ülker E (2020) Optimization of parameters of CNN based method by particle swarm optimization. Int J Adv Comput Eng Netw (IJACEN) 8:1–4
İnik Ö, Ceyhan A, Balcıoğlu E, Ülker E (2019) A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network. Comput Biol Med 112:103350
İnik Ö, Altiok M, Ülker E, Koçer B (2021) MODE-CNN: a fast converging multi-objective optimization algorithm for CNN-based models. Appl Soft Comput 109:107582
Jayakumar N, Subramanian S, Ganesan S, Elanchezhian EB (2016) Grey wolf optimization for combined heat and power dispatch with cogeneration systems. Int J Electr Power 74:252–264. https://doi.org/10.1016/j.ijepes.2015.07.031
Junior FEF, Yen GG (2019) Particle swarm optimization of deep neural networks architectures for image classification. Swarm Evol Comput 49:62–74
Kainz P, Pfeiffer M, Urschler M (2015) Semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation. arXiv preprint arXiv:151106919
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer …,
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132
Kashif MN, Raza SEA, Sirinukunwattana K, Arif M, Rajpoot N (2016) Handcrafted features with convolutional neural networks for detection of tumor cells in histology images. In: Biomedical imaging (ISBI), 2016 IEEE 13th international symposium on, 2016. IEEE, pp 1029–1032
Ker J, Wang L, Rao J, Lim T (2018) Deep learning applications in medical image analysis. IEEE Access 6:9375–9389
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105
Lee WY, Park SM, Sim KB (2018) Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm. Optik 172:359–367
Lorenzo PR, Nalepa J, Kawulok M, Ramos LS, Pastor JR (2017) Particle swarm optimization for hyper-parameter selection in deep neural networks. In: Proceedings of the genetic and evolutionary computation conference. pp 481–488
Ma B, Li X, Xia Y, Zhang Y (2020) Autonomous deep learning: a genetic DCNN designer for image classification. Neurocomputing 379:152–161
Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 3D Vision (3DV), 2016 fourth international conference on, 2016. IEEE, pp 565–571
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Išgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35:1252–1261
Nalepa J, Lorenzo PR (2017) Convergence analysis of PSO for hyper-parameter selection in deep neural networks. In: International conference on P2P, Parallel, Grid, Cloud and Internet Computing. Springer, pp 284–295
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66
Quinn JA, Nakasi R, Mugagga PK, Byanyima P, Lubega W, Andama A (2016) Deep convolutional neural networks for microscopy-based point of care diagnostics. In: Machine learning for healthcare conference. pp 271–281
Roerdink JB, Meijster A (2000) The watershed transform: definitions, algorithms and parallelization strategies. Fundam Inform 41:187–228
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241
Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 42:990–1002
Sirinukunwattana K, Raza SEA, Tsang Y-W, Snead DR, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35:1196–1206
Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: Neural networks (IJCNN), 2016 international joint conference on, 2016. IEEE, pp 2560–2567
Sun Y, Xue B, Zhang M, Yen GG (2018) A particle swarm optimization-based flexible convolutional autoencoder for image classification. IEEE Trans Neural Netw Learn Syst 30:2295–2309
Sun Y, Xue B, Zhang M, Yen GG (2019) Evolving deep convolutional neural networks for image classification. IEEE Trans Evolut Comput. https://doi.org/10.1109/TEVC.2019.2916183
Wang H et al (2014a) Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging 1:034003
Wang L, Shi F, Li G, Gao Y, Lin W, Gilmore JH, Shen D (2014b) Segmentation of neonatal brain MR images using patch-driven level sets. NeuroImage 84:141–158
Wang J, MacKenzie JD, Ramachandran R, Chen DZ (2016) A deep learning approach for semantic segmentation in histology tissue images. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 176–184
Wang B, Sun Y, Xue B, Zhang M (2018) Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification. In: 2018 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8
Xie Y, Xing F, Kong X, Su H, Yang L (2015) Beyond classification: structured regression for robust cell detection using convolutional neural network. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 358–365
Xie W, Noble JA, Zisserman A (2018) Microscopy cell counting and detection with fully convolutional regression networks. Comput Methods Biomech Biomed Eng Imaging Vis 6:283–292
Yamasaki T, Honma T, Aizawa K (2017) Efficient optimization of convolutional neural networks using particle swarm optimization. In: 2017 IEEE third international conference on multimedia big data (BigMM). IEEE, pp 70–73
Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108:214–224
Funding
This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK). Funds is 1512—Entrepreneurship Multi-Phase Programme with project number 2180141.
Author information
Authors and Affiliations
Contributions
Conceptualization: [Öİ], [EÜ]; Methodology: [Öİ], [EÜ]; Formal analysis and investigation: [Öİ], [EÜ]; Project administration: [Öİ]; Software: [Öİ]; Visualization: [Öİ]; Validation: [Öİ], [EÜ]; Resources: [Öİ]; Data curation: [Öİ], [EÜ]; Writing—Original Draft: [Öİ]; Writing- Reviewing and Editing [Öİ], [EÜ]; Supervision: [EÜ].
Corresponding author
Ethics declarations
Conflict of interest
There is no conflict of interest between the authors and publish this manuscript.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Inik, Ö., Ülker, E. Optimization of deep learning based segmentation method. Soft Comput 26, 3329–3344 (2022). https://doi.org/10.1007/s00500-021-06711-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-021-06711-3