Bionic Artificial Neural Networks in Medical Image Analysis

Wang, Shuihua; Chen, Huiling; Zhang, Yudong

doi:10.3390/biomimetics8020211

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…The second issue is that conventional machine learning algorithms have performed poorly when used to analyze large amounts of data, particularly when it comes to the evaluation of medical data, which includes text notes and diagnostic images which is one of the primary medical tools used to estimate and inhibit human infection. Last but not least, a lot of medical data, including genetic expressions and bio-signals, have significant levels of noise and fluctuation, which enable information retrieval particularly challenging (Brahimi et al, 2018 ; Wang et al, 2023 ). Medical images are the primary diagnostic and prognostic tool utilized by physicians, which illustrates why statistical models are required for the interpretation of such material.…”

Section: Introductionmentioning

confidence: 99%

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification

Iqbal,

Qureshi,

Alhussein

et al. 2024

Front. Comput. Neurosci.

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The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.

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Section: Introductionmentioning

confidence: 99%

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification

Iqbal,

Qureshi,

Alhussein

et al. 2024

Front. Comput. Neurosci.

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“…At present, many researchers have achieved good simulations of real walking situations, with high bionicity [ 10 , 11 ]. In addition, humanoid robots have been widely used in various fields [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of a Car–Running Pedestrian Accident Based on a Humanoid Robot Method

Wang,

Wei,

Wei

et al. 2023

Sensors

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Due to the characteristics of multibody (MB) and finite element (FE) digital human body models (HBMs), the reconstruction of running pedestrians (RPs) remains a major challenge in traffic accidents (TAs) and new innovative methods are needed. This study presents a novel approach for reconstructing moving pedestrian TAs based on a humanoid robot method to improve the accuracy of analyzing dynamic vehicle–pedestrian collision accidents. Firstly, we applied the theory of humanoid robots to the corresponding joints and centroids of the TNO HBM and implemented the pedestrian running process. Secondly, we used rigid–flexible coupling HBMs to build pedestrians, which can not only simulate running but also analyze human injuries. Then, we validated the feasibility of the RP reconstruction method by comparing the simulated dynamics with the pedestrian in the accident. Next, we extracted the velocity and posture of the pedestrian at the moment of collision and further validated the modeling method through a comparison of human injuries and forensic autopsy results. Finally, by comparing two other cases, we can conclude that there are relative errors in both the pedestrian injury results and the rest position. This comparative analysis is helpful for understanding the differences in injury characteristics between the running pedestrian and the other two cases in TAs.

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Multi-strategy augmented Harris Hawks optimization for feature selection

Zhao,

Yu,

Guo

et al. 2024

Journal of Computational Design and Engineering

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In the context of increasing data scale, contemporary optimization algorithms struggle with cost and complexity in addressing the feature selection (FS) problem. This paper introduces a Harris hawks optimization (HHO) variant, enhanced with a multi-strategy augmentation (CXSHHO), for FS. The CXSHHO incorporates a communication and collaboration strategy (CC) into the baseline HHO, facilitating better information exchange among individuals, thereby expediting algorithmic convergence. Additionally, a directional crossover (DX) component refines the algorithm's ability to thoroughly explore the feature space. Furthermore, the soft-rime strategy (SR) broadens population diversity, enabling stochastic exploration of an extensive decision space and reducing the risk of local optima entrapment. The CXSHHO's global optimization efficacy is demonstrated through experiments on 30 functions from CEC2017, where it outperforms 15 established algorithms. Moreover, the paper presents a novel FS method based on CXSHHO, validated across 18 varied datasets from UCI. The results confirm CXSHHO's effectiveness in identifying subsets of features conducive to classification tasks.

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Bionic Artificial Neural Networks in Medical Image Analysis

Abstract: Bionic artificial neural networks (BANNs) are a type of artificial neural network (ANN) [...]

Cited by 3 publications

References 24 publications

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification

Reconstruction of a Car–Running Pedestrian Accident Based on a Humanoid Robot Method

Multi-strategy augmented Harris Hawks optimization for feature selection

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