Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2408
Title: Optimization of deep learning based segmentation method
Authors: İnik, Özkan
Ülker, Erkan
Keywords: Artificial bee colony (ABC)
Black widow optimization (BWO)
CNN
Deep learning
Genetic algorithm (GA)
Grey wolf optimizer (GWO)
Parameter optimization
Particle swarm optimization (PSO)
Segmentation
Convolutional Neural-Networks
Classification
Selection
Nuclei
Search
Images
Issue Date: 2022
Publisher: Springer
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.
URI: https://doi.org/10.1007/s00500-021-06711-3
https://hdl.handle.net/20.500.13091/2408
ISSN: 1432-7643
1433-7479
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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