Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

Guo-wei, Wang; Guo, Sen; Han, Lina; Cekderi, Anil Baris

doi:10.1016/j.bspc.2022.103933

Cited by 21 publications

(12 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2022, Guowei et al [ 22 ] proposed a novel lung parenchyma segmentation method is introduced, integrating a two-dimensional reciprocal cross-entropy multi-threshold approach with an enhanced firefly algorithm. An optimal threshold method was initially applied for lung segmentation, enabling dynamic adjustments in segmentation thresholds based on detailed anatomical features like ground-glass opacity, lung lobes, bronchi, and trachea.…”

Section: Literature Reviewmentioning

confidence: 99%

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

Shafi,

Chinnappan

2024

PLoS ONE

View full text Add to dashboard Cite

Diagnosing lung diseases accurately and promptly is essential for effectively managing this significant public health challenge on a global scale. This paper introduces a new framework called Modified Segnet-based Lung Disease Segmentation and Severity Classification (MSLDSSC). The MSLDSSC model comprises four phases: "preprocessing, segmentation, feature extraction, and classification." Initially, the input image undergoes preprocessing using an improved Wiener filter technique. This technique estimates the power spectral density of the noisy and original images and computes the SNR assisted by PSNR to evaluate image quality. Next, the preprocessed image undergoes Segmentation to identify and separate the RoI from the background objects in the lung image. We employ a Modified Segnet mechanism that utilizes a proposed hard tanh-Softplus activation function for effective Segmentation. Following Segmentation, features such as MLDN, entropy with MRELBP, shape features, and deep features are extracted. Following the feature extraction phase, the retrieved feature set is input into a hybrid severity classification model. This hybrid model comprises two classifiers: SDPA-Squeezenet and DCNN. These classifiers train on the retrieved feature set and effectively classify the severity level of lung diseases.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

Shafi,

Chinnappan

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Wang et al, ( Guowei Wang et al, 2022 ) proposed a new hybrid algorithm for segmenting COVID-19 chest X-ray images by combining the PSO and firefly algorithm (FA). Multi-threshold segmentation technique based on two-dimensional reciprocal cross-entropy is suggested to solve the problem of undefined and zero values of Shannon cross entropy due to logarithm operation.…”

Section: Related Workmentioning

confidence: 99%

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Sahoo¹,

Houssein²,

Premkumar³

et al. 2023

Expert Systems with Applications

View full text Add to dashboard Cite

“…However, a general problem in these methods is that it is complicated to choose their optimal parameters [37] . For example, selecting the size of the operator in image preprocessing is challenging [33] .…”

Section: Related Workmentioning

confidence: 99%

COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold

Rao

Zeng

et al. 2023

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

Cited by 21 publications

References 57 publications

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold

Contact Info

Product

Resources

About