Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet

Chen, Ying; Gan, Huimin; Chen, Huiling; Zeng, Yugang; Xu, Liang; Heidari, Ali Asghar; Zhu, Xiaodong; Liu, Yuanning

doi:10.1016/j.neucom.2022.10.064

Cited by 73 publications

(16 citation statements)

References 27 publications

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“…As a result, it is undeniably established that MGACO is a very good swarm intelligence optimization algorithm and that MGACO-MIS is an even better segmentation technique when MGACO is used to segment problematic pictures from COVID-19. In future work, the proposed method can also be applied to more cases, such as the optimization of machine learning models iris segmentation and recognition (Chen et al, 2023), fine-grained alignment (Wang et al, 2023), remote pulse extraction (Zhao et al, 2022), Alzheimer's disease identification (Yan et al, 2022), MRI reconstruction (Lv et al, 2021), renewable energy generation (Sun et al, 2022), power distribution network (Cao et al, 2022), retinal vessel segmentation (Li et al, 2022), privacy protection of personalized information retrieval (Wu Z. et al, 2020;Wu et al, 2021cWu et al, ,d, 2023, and privacy protection of location-based services (Wu et al, 2021b(Wu et al, , 2022.…”

Section: Discussionmentioning

confidence: 99%

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

Zhao

Liu

Heidari

et al. 2023

Front. Neuroinform.

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The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur’s entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.

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Section: Discussionmentioning

confidence: 99%

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

Zhao

Liu

Heidari

et al. 2023

Front. Neuroinform.

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“…In the next step, we will investigate participants’ physiological data under different cognitive domains to assess whether they can be regarded as predictors of cognitive performance. Finally, our classification framework is based on traditional machine learning methods and in the future, as the sample increases, we propose to improve the classification performance further using deep learning models ( Zhao et al, 2022 ; Chen et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Zhang

et al. 2023

Front. Hum. Neurosci.

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IntroductionAdvances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients’ behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.MethodsWe collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.ResultsThe experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.DiscussionIt was found that patients’ classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

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“…As a result, the bSCBA-KELM model is anticipated to be a reliable and effective tool for classifying and predicting toxicology. The SCBA-KELM model will be used in further research to address problems with disease diagnosis, 56,57 image segmentation, 58,59 image reconstruction, 60,61 optimization of machine learning models, service ecosystem, 62 power distribution network, 19 computational experiments, 63 and so on.…”

Section: Discussionmentioning

confidence: 99%

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Su,

Zhao,

Heidari

et al. 2023

Basic Clin Pharma Tox

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Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover, and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2, and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.

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Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet

Cited by 73 publications

References 27 publications

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

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