Gaussian Barebone Salp Swarm Algorithm with Stochastic Fractal Search for medical image segmentation: A COVID-19 case study

“…The PSNR, SSIM index, and FSIM index is applied to further evaluate image segmentation quality (Liu et al, 2021b ; Shi et al, 2021 ; Zhang et al, 2021 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

et al. 2021

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Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

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“…Ant colony optimization is proposed which improves the speed of the convergence and also the capacity of the search highly which in turn effectively diagnosing COVID‐19 in Xrays (Liu et al, 2021). Similarly, a swarm intelligent optimization algorithm is proposed to perform better segmentation in COVID‐19 CT images (Zhang, Wang, et al, 2021).…”

Section: Literature Surveymentioning

confidence: 99%

Acost‐sensitivedeep learning‐basedmeta‐classifierfor pediatric pneumonia classification using chest X‐rays

2022

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Literature survey shows that convolutional neural network (CNN)‐based pretrained models have been successfully employed to diagnose and detect childhood pneumonia using chest X‐rays (CXR). However, most of the existing methods are prone to imbalance problems, which become even more significant in medical image classification for example most importantly childhood pneumonia classification using CXR. This is due to the fact that some classes in childhood pneumonia have a very little support in the training dataset. Additionally, though the existing methods have reported better performances for training and testing, in most of the test cases the existing models will not be effective on variants of the childhood pneumonia CXR images or CXR samples from a new pediatric patient. In addition, the models may be effective in detecting latent stage pediatric pneumonia but not show better performances for CXR samples from pediatric patients who are early stage, sick but not pneumonia, sick with other lung diseases, and so on. Generalization is an important term to be considered while designing a pneumonia classifier that can perform well on completely unseen pneumonia CXR datasets. This article presents a cost‐sensitive large‐scale learning with stacked ensemble meta‐classifier and transfer learning‐based deep feature fusion approach for pediatric pneumonia classification using CXR. With the aim to identify the importance among the classes of pneumonia, the larger cost items are introduced based on the class‐imbalance degree during the backpropogation learning methodology in transfer learning models such as Xception, InceptionResNetV2, DenseNet201, and NASNetMobile. Next, the features from the penultimate layer (global average pooling) of Xception, InceptionResNetV2, DenseNet201, and NASNetMobile were extracted and dimensionality of the extracted features were reduced using kernel principal component analysis (KPCA). The reduced features were fused together and passed into a stacked ensemble meta‐classifier for classifying the CXR into either pneumonia or normal. A stacked ensemble meta‐classifier is a two stage approach in which the first stage employs random forest and support vector machine (SVM) for prediction and followed by logistic regression for classification. Experiments of the proposed model were done on publicly available benchmark pediatric pneumonia classification CXR dataset. In addition, the experiments for existing methods as well as various cost‐insensitive models were conducted. In all the experiments, the proposed method has achieved better performances compared to the existing methods as well as various cost‐insensitive models. In particular, the proposed method showed 6% improvement in precision, 10% improvement in recall, 9% improvement in F1 score with less misclassification costs (0.0321) and accuracy (96.8%). Most importantly, the proposed method is insensitive to the imbalance data and more effective to handle variants of the childhood pneumonia CXR images. Thus, the proposed approach can...

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Gaussian Barebone Salp Swarm Algorithm with Stochastic Fractal Search for medical image segmentation: A COVID-19 case study

Cited by 38 publications

References 132 publications

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Acost‐sensitivedeep learning‐basedmeta‐classifierfor pediatric pneumonia classification using chest X‐rays

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