Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

Dhal, Krishna Gopal; Ray, Swarnajit; Barik, Sudip; Das, Arunita

doi:10.1007/s42235-023-00392-4

Cited by 4 publications

(3 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the studies of [33][34][35][36][37][38][39][40], the researchers used basic OBL techniques to improve their exploration of SMA variants.…”

Section: Opposition-based Learning (Obl)mentioning

confidence: 99%

“…Dipak Kumar Patra et al [34] developed an enhanced SMA by incorporating the quasi opposition-based learning (QOBL) mechanism in their study. Krishna Gopal Dhal et al [35] proposed an improved SMA (called ISMA) based on a random OBL and DE's mutation strategy. Liang Xu et al [36] presented an ISMA by adding an improved random OBL to increase convergence ability in the late iteration and prevent local optimizations from occurring.…”

Section: Opposition-based Learning (Obl)mentioning

confidence: 99%

“…In addition to the multi-threshold image segmentation, the researchers used image segmentation technology to assist in performing the medical diagnoses [34,96,150]. Krishna Gopal Dhal et al [35] used an ISMA to perform illumination-free white blood cell (WBC) segmentations.…”

Section: Image Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

View full text Add to dashboard Cite

The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

show abstract

“…In the studies of [33][34][35][36][37][38][39][40], the researchers used basic OBL techniques to improve their exploration of SMA variants.…”

Section: Opposition-based Learning (Obl)mentioning

confidence: 99%