A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

Rundo, Leonardo; Militello, Carmelo; Vitabile, Salvatore; Russo, Giorgio; Sala, Evis; Gilardi, Maria Carla

doi:10.3233/fi-2020-1887

Cited by 42 publications

(32 citation statements)

References 125 publications

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“…Evolutionary algorithms have always played a critical role in medical image analysis by reducing the overall computation required by intensive DL or ML algorithms. Rundo et al [43] surveyed the literature for the state-ofart of nature-inspired medical images analysis methods focusing on bio-medical data integration. Mostafa et al [44] used whale optimization algorithm to segment liver from MRI scans by extracting features from different segments of the image with an accuracy of 97.5%.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

et al. 2021

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A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is essential to reduce further spread of the disease, but due to a shortage of testing kits and limited supply, alternative testing methods are being evaluated. Recently researchers have found that chest X-Ray (CXR) images provide salient information about COVID-19. An intelligent system can help the radiologists to detect COVID-19 from these CXR images which can come in handy at remote locations in many developing nations. In this work, we propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm. The selected features were then used to classify the CXR images using a number of classifiers. The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

et al. 2021

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“…Each member of the group changes the direction of search by continuously accumulating experience, randomly generating, evolving and updating a large number of possible solutions, until the stopping criterion is reached, then the search stops. The swarm intelligence optimization strategy has the characteristics of fast solving speed, high accuracy, wide application range, and strong stability, and it is widely used in the parameter optimization of machine-learning algorithms [ 39 , 40 ]. Typical swarm intelligence optimization includes PSO [ 41 ], Ant Colony Optimization (ACO) [ 42 ], Artificial Bee Colony (ABC) [ 43 ], etc.…”

Section: Related Workmentioning

confidence: 99%

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

Chang

Zheng

2021

Computational Intelligence and Neuroscience

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Feature selection and lung nodule recognition are the core modules of the lung computer-aided detection (Lung CAD) system. To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. First, in view of the poor interpretability of deep features and the incomplete expression of clinically defined handcrafted features, a feature cascade method is proposed to obtain richer feature information of nodules as the final input of the classifier. Second, to better map the global characteristics of samples, the multiple kernel learning support vector machine (MKL-SVM) algorithm with a linear convex combination of polynomial kernel and sigmoid kernel is proposed. Furthermore, this paper applied the methods for speed contraction factor and roulette strategy, and a mixture of simulated annealing (SA) and particle swarm optimization (PSO) is used for global optimization, so as to solve the problem that the PSO is easy to lose particle diversity and fall into the local optimal solution as well as improve the model’s training speed. Therefore, the MKL-SVM algorithm is presented in this paper, which is based on swarm intelligence optimization is proposed for lung nodule recognition. Finally, the algorithm construction experiments are conducted on the cooperative hospital dataset and compared with 8 advanced algorithms on the public dataset LUNA16. The experimental results show that the proposed algorithms can improve the accuracy of lung nodule recognition and reduce the missed detection of nodules.

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“…As stated, NI&M is a specific computational approach within SC which has been extensively applied in addressing many real-world optimization problems as those within CV. The reader can find an extensive overview on the topic in references [11,12,18].…”

Section: Nature-inspired and Metaheuritcs-based Image Registrationmentioning

confidence: 99%

“…Additionally, metaheuristics are other general purpose optimization algorithms successfully applied in this field. As an example, several special issues and books on the topic have been published in international forums in the last few years [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

et al. 2020

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The development of automated image registration (IR) methods is a well-known issue within the computer vision (CV) field and it has been largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. In the last two decades, there has been an outstanding interest in the application of new optimization approaches for dealing with the main drawbacks present in the early IR methods, e.g., the Iterative Closest Point (ICP) algorithm. In particular, nature-inspired computation, e.g., evolutionary computation (EC), provides computational models that have their origin in evolution theories of nature. Moreover, other general purpose algorithms known as metaheuristics are also considered in this category of methods. Both nature-inspired and metaheuristic algorithms have been extensively adopted for tackling the IR problem, thus becoming a reliable alternative for optimization purposes. In this contribution, we aim to perform a comprehensive overview of the last decade (2009–2019) regarding the successful usage of this family of optimization approaches when facing the IR problem. Specifically, twenty-four methods (around 16 percent) of more than one hundred and fifty different contributions in the state-of-the-art have been selected. Several enhancements have been accordingly provided based on the promising outcomes shown by specific algorithmic designs. Finally, our research has shown that the field of nature-inspired and metaheuristic algorithms has increased its interest in the last decade to address the IR problem, and it has been highlighted that there is still room for improvement.

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A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration

Cited by 42 publications

References 125 publications

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL‐SVM

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

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