ArticleJournal of bionic engineering2023
Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing.
Article in Journal of bionic engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 36 citations in OpenAlex.
- Bioinspired hybrid optimisation and deep belief neural networks for early chronic kidney disease detection: an explainable clinical AI framework.Renal failure · 2026Article
- Improved genetic algorithm for multi-threshold optimization in digital pathology image segmentation.Scientific reports · 2024Article
- An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines.Sensors (Basel, Switzerland) · 2023Article
- Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design.iScience · 2023Article
- Face Image Segmentation Using Boosted Grey Wolf Optimizer.Biomimetics (Basel, Switzerland) · 2023Article
- Improved Colony Predation Algorithm Optimized Convolutional Neural Networks for Electrocardiogram Signal Classification.Biomimetics (Basel, Switzerland) · 2023Article
- An enhanced decision-making framework for predicting future trends of sharing economy.PloS one · 2023Article
- Multi-threshold image segmentation of 2D OTSU inland ships based on improved genetic algorithm.PloS one · 2023Article
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Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
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Abstract
Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.
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