Networks For Image Classification Research Articles

Cross-Hopping Graph Networks for Hyperspectral–High Spatial Resolution (H2) Image Classification

As we take stock of the contemporary issue, remote sensing images are gradually advancing towards hyperspectral–high spatial resolution (H2) double-high images. However, high resolution produces serious spatial heterogeneity and spectral variability while improving image resolution, which increases the difficulty of feature recognition. So as to make the best of spectral and spatial features under an insufficient number of marking samples, we would like to achieve effective recognition and accurate classification of features in H2 images. In this paper, a cross-hop graph network for H2 image classification(H2-CHGN) is proposed. It is a two-branch network for deep feature extraction geared towards H2 images, consisting of a cross-hop graph attention network (CGAT) and a multiscale convolutional neural network (MCNN): the CGAT branch utilizes the superpixel information of H2 images to filter samples with high spatial relevance and designate them as the samples to be classified, then utilizes the cross-hop graph and attention mechanism to broaden the range of graph convolution to obtain more representative global features. As another branch, the MCNN uses dual convolutional kernels to extract features and fuse them at various scales while attaining pixel-level multi-scale local features by parallel cross connecting. Finally, the dual-channel attention mechanism is utilized for fusion to make image elements more prominent. This experiment on the classical dataset (Pavia University) and double-high (H2) datasets (WHU-Hi-LongKou and WHU-Hi-HongHu) shows that the H2-CHGN can be efficiently and competently used in H2 image classification. In detail, experimental results showcase superior performance, outpacing state-of-the-art methods by 0.75–2.16% in overall accuracy.

A multiscale dilated attention network for hyperspectral image classification

Hyperspectral imaging is an image obtained by combining spectral detection technology and imaging technology, which can collect electromagnetic spectra in the wavelength range of visible light to near-infrared. It is an important research content in the field of ground observation in hyperspectral remote sensing. However, hyperspectral image face significant challenges in classification task due to their high spectral dimensions, lack of labeled samples, and strong correlation between bands. In order to fully extract features from both spectral and spatial dimensions and improve classification accuracy in the case of limited training samples, a multiscale dilated attention network is proposed for hyperspectral image classification. First, a three-dimensional convolutional layer is used to extract the shallow features of the image. Then, a multiscale dilated attention module is proposed by combining dilated convolution and channel attention. Using ordinary convolution and dilated convolution to form different receptive fields. Channel attention is used to remodel the obtained multiscale features, enhancing the inter-channel correlation. After that, a multiscale spatial-spectral attention module is constructed using multiple asymmetric convolutions to obtain spatial and spectral attention features at different positions, further enhancing important feature suppression over non-important features. Finally, using softmax to classify the obtained features. Using Indian Pines, Pavia University, KSC and University of Houston as experimental datasets, the overall classification accuracy of this paper’s method achieved 98.97%, 99.14%, 99.45%, and 98.56% respectively, using only 5%, 1%, 10%, and 10% of training samples per class. Compared with seven advanced classification methods, the experimental results show that the proposed method can achieve the highest classification accuracy with limited training samples.

A consistency-aware deep capsule network for hierarchical multi-label image classification

Hierarchical classification is a significant challenge in computer vision due to the logical order and interconnectedness of multiple labels. This paper presents HD-CapsNet, a novel neural network architecture based on deep capsule networks, specifically designed for hierarchical multi-label classification(HMC). By incorporating a tree-like hierarchical structure, HD-CapsNet is designed to leverage the inherent ontological order within the hierarchical label tree, thereby ensuring classification consistency across different levels. Additionally, we introduce a specialized loss function that promotes accurate hierarchical relationships while penalizing inconsistencies. This not only enhances classification performance but also strengthens the network’s robustness. We rigorously evaluate HD-CapsNet’s efficacy by benchmarking it against existing HMC methods across six diverse datasets: Fashion-MNIST, Marine-Tree, CIFAR-10, CIFAR-100, Caltech-UCSD Birds-200-2011, and Stanford Cars. Our results conclusively demonstrate that HD-CapsNet excels in learning hierarchical relationships and significantly outperforms the competition in various image classification tasks. Our implementation is available at https://github.com/tasrif-khondaker/HD-CapsNet.

A local enhanced mamba network for hyperspectral image classification

Deep learning has significantly advanced hyperspectral image (HSI) classification, primarily due to its robust nonlinear feature extraction capabilities. The vision transformer has achieved notable performance but is limited by the quadratic computational burden of its self-attention mechanism. Recently, a network based on state space model named Mamba, has attracted considerable attention for its linear complexity and commendable performance. Nevertheless, Mamba was originally designed for one-dimensional causal sequence modeling, and its effectiveness in inherent non-causal HSI classification remains to be fully validated. To address this issue, we propose a novel Local Enhanced Mamba (LE-Mamba) network for hyperspectral image classification, which mainly comprises a Local Enhanced Spatial SSM (LES-S6), a Central Region Spectral SSM (CRS-S6), and a Multi-Scale Convolutional Gated Unit (MSCGU). The LES-S6 improves non-causal local feature extraction by incorporating a multi-directional local spatial scanning mechanism. Additionally, the CRS-S6 employs a bidirectional scanning mechanism in the spectral dimension to capture fine spectral details and integrate them with spatial information. The MSCGU utilizes a convolutional gating mechanism to aggregate features from diverse scanning routes and extract high-level semantic information. The overall accuracies of LE-Mamba on Indian Pines, WHU-Hi-HanChuan, WHU-Hi-LongKou, and Pavia University datasets are 99.16 %, 98.16 %, 99.57 %, and 99.63 %, respectively. Extensive experimental results on these four public datasets demonstrate that the LE-Mamba outperforms eight mainstream deep learning models in classification performance.

Image Classification Based on Low-Level Feature Enhancement and Attention Mechanism

Deep learning-based image classification networks heavily rely on the extracted features. However, as the model becomes deeper, important features may be lost, resulting in decreased accuracy. To tackle this issue, this paper proposes an image classification method that enhances low-level features and incorporates an attention mechanism. The proposed method employs EfficientNet as the backbone network for feature extraction. Firstly, the Feature Enhancement Module quantifies and statistically processes low-level features from shallow layers, thereby enhancing the feature information. Secondly, the Convolutional Block Attention Module enhances the high-level features to improve the extraction of global features. Finally, the enhanced low-level features and global features are fused to supplement low-resolution global features with high-resolution details, further improving the model’s image classification ability. Experimental results illustrate that the proposed method achieves a Top-1 classification accuracy of 86.49% and a Top-5 classification accuracy of 96.90% on the ETH-Food101 dataset, 86.99% and 97.24% on the VireoFood-172 dataset, and 70.99% and 92.73% on the UEC-256 dataset. These results demonstrate that the proposed method outperforms existing methods in terms of classification performance.

Transfer of learning in convolutional neural networks for thermal image classification in Electrical Transformer Rooms

Overheating of power transformers, low-voltage panels, and medium-voltage components in Electric Transformer Rooms (ETRs) can result from various factors, such as contact issues, irregular loads, and other similar problems. Thermal imaging shows significant potential for detecting faults in power equipment. However, its effectiveness is hindered by the complex thermal patterns of faults and variability in equipment and environmental conditions, making accurate fault detection challenging. This paper aims to study the effectiveness of transfer learning architectures for automating equipment classification in ETRs. This work applies four transfer learning architectures: AlexNet, SqueezeNet, VGG19, and GoogLeNet. The findings of the testing phase demonstrated that the use of transfer learning by fine-tuning pre-trained convolutional neural networks was highly effective in the classification of thermal images captured from ETRs, with the models achieving accuracy rates between 86.98% and 100%, and F1-Scores between 86.79% and 100%.

A novel individual-relational consistency for bad semi-supervised generative adversarial networks (IRC-BSGAN) in image classification and synthesis

A novel individual-relational consistency for bad semi-supervised generative adversarial networks (IRC-BSGAN) in image classification and synthesis

CATNet: Cascaded attention transformer network for marine species image classification

Complex physicochemical environmental effects result in the underwater species images’ highly intricate and diverse backgrounds, which poses significant challenges for identifying marine species. Deep learning techniques are used extensively for image classification thanks to their exceptional feature extraction capabilities. To address the above issues, we present a cascaded attention transformer network for marine species image classification named CATNet. More specifically, we first utilize the efficient channel attention module with depthwise convolution, which can efficiently extract the essential features of the image and reduce the computational parameters of the traditional attention module. Subsequently, we design a stacked transformer module to focus on the different features among a large amount of feature information, thereby reducing redundancy and improving?the generalization performance of CATNet. To comprehensively exploit the features extracted from different modules, we integrate the efficient channel attention and stacked transformer modules to construct a feature-cascaded module. Meanwhile, we construct a large-scale Marine Species Image Dataset (MSID) including 8605 underwater images with different species. Concretely, it consists of 712 coral reef images, 1270 fish images, 1082 people images, 2414 sea urchin images, 1219 squid images, 837 sea cucumber images, and 1071 turtle images. Comprehensive experimentation on our built dataset underscores the proposed CATNet’s superiority over the existing state-of-the-art methods in marine species image classification. The data is available. https://www.researchgate.net/publication/381961074_2024MSID.

EfficientNet and mixed convolution network for three-class brain tumor magnetic resonance image classification

EfficientNet and mixed convolution network for three-class brain tumor magnetic resonance image classification

A Multiobjective Genetic Algorithm to Evolving Local Interpretable Model-Agnostic Explanations for Deep Neural Networks in Image Classification

Deep convolutional neural networks have become a dominant solution for numerous image classification tasks. However, a main criticism is the poor explainability due to the black-box characteristic, which hurdles the extensive usage of deep convolutional neural networks. To address this issue, this paper proposes a new evolutionary multi-objective based method, which aims to explain the behaviours of deep convolutional neural networks by evolving local explanations on specific images. To the best of our knowledge, this is the first evolutionary multi-objective method to evolve local explanations. The proposed method is model-agnostic, i.e. it is applicable to explain any deep convolutional neural networks. ImageNet is used to examine the effectiveness of the proposed method. Three well-known deep convolutional neural networks -VGGNet, ResNet, and MobileNet, are chosen to demonstrate the modelagnostic characteristic. Based on the experimental results, it can be observed that the local explanations are understandable to end-users, who need to check the sensibility of the evolved explanations to decide whether to trust the predictions made by the deep convolutional neural networks. Furthermore, the local explanations evolved by the proposed method improves the confidence of deep convolutional neural networks making the predictions. Lastly, the pareto front and convergence analyses indicate that the proposed method can form a good set of nondominated solutions.

A branched Convolutional Neural Network for RGB-D image classification of ceramic pieces

From smart sensors on assembly lines to robots performing complex tasks, the fourth industrial revolution is rapidly transforming manufacturing. The growing prominence of 3D cameras in the industry has led the computer vision community to explore innovative ways of integrating depth and color data to achieve higher precision, essential for ensuring product quality in manufacturing. In this study, we introduce an innovative branched convolutional neural network designed to produce high-speed classification of multimodal images, such as RGB-Depth (RGB-D) images. The fundamental concept underlying the branched approach is the specialization of each branch as a dedicated feature extractor for a single modality, followed by their merge (intermediate fusion) to enable effective classification. Feeding our model is our novel multimodal dataset, named CeramicNet, composed of 8 classes that include RGB, depth, and RGB-D variations to enable extensive experimentation and evaluation of the models which, to the best of our knowledge, has not been previously introduced in the computer vision community. We conducted a series of experiments on the CeramicNet dataset. These experiments aimed at fine-tuning the model, assessing the influence of various depth technologies, exploring individual modalities, examining their collective impact, and performing comprehensive data analysis. Comparing our solution against seven widely used models, we achieved remarkable results, securing the top position with a precision of 99.89, with a lead of over 1% against the nearest competitor. What is more, the proposed solution yields an inference time of 127.6 ms — being nearly three times faster than the second-best performer.

Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification

Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification

An Adaptive Migration Collaborative Network for Multimodal Image Classification.

The multispectral (MS) and the panchromatic (PAN) images belong to different modalities with specific advantageous properties. Therefore, there is a large representation gap between them. Moreover, the features extracted independently by the two branches belong to different feature spaces, which is not conducive to the subsequent collaborative classification. At the same time, different layers also have different representation capabilities for objects with large size differences. In order to dynamically and adaptively transfer the dominant attributes, reduce the gap between them, find the best shared layer representation, and fuse the features of different representation capabilities, this article proposes an adaptive migration collaborative network (AMC-Net) for multimodal remote-sensing (RS) images classification. First, for the input of the network, we combine principal component analysis (PCA) and nonsubsampled contourlet transformation (NSCT) to migrate the advantageous attributes of the PAN and the MS images to each other. This not only improves the quality of images themselves, but also increases the similarity between the two images, thereby reducing the representational gap between them and the pressure on the subsequent classification network. Second, for the interaction on the feature migrate branch, we design a feature progressive migration fusion unit (FPMF-Unit) based on the adaptive cross-stitch unit of correlation coefficient analysis (CCA), which can make the network automatically learn the features that need to be shared and migrated, aiming to find the best shared-layer representation for multifeature learning. And we design an adaptive layer fusion mechanism module (ALFM-Module), which can adaptively fuse features of different layers, aiming to clearly model the dependencies among multiple layers for different sized objects. Finally, for the output of the network, we add the calculation of the correlation coefficient to the loss function, which can make the network converge to the global optimum as much as possible. The experimental results indicate that AMC-Net can achieve competitive performance. And the code for the network framework is available at: https://github.com/ru-willow/A-AFM-ResNet.

Image restoration model for microscopic defocused images based on blurring kernel guidance

Defocus blurring imaging seriously affects the observation accuracy and application range of optical microscopes, and the blurring kernel function is a key parameter for high-resolution image restoration. However, its solving process is complicated and high in computational cost. Image restoration based on most neural networks has high requirements on data sets and the image resolution after restoration is limited because of the lack of quantitative estimation of blurring kernels. In this study, an image restoration method guided by blurring kernel estimation for microscopic defocused images is proposed. First, to reduce the blurring kernel estimation error caused by the positive and negative difference in microscopic defocused imaging, a defocused image classification network is designed to classify the input defocused images with different defocus distances and directions, and its output images are input into the blurring kernel extraction network composed of the feature extraction, correlation, and blurring kernel reconstruction layers. Second, a non-blind defocused image restoration model to restore the high-resolution images is proposed by introducing the blurring kernel extraction module into the restoration network based on U-Net, and the blurring kernel estimation and image restoration losses are jointly trained to realize image restoration guided by blurring kernel estimation. Finally, the experimental results of our proposed method demonstrate significant improvements in both the peak signal-to-noise ratio and structural similarity index measure when compared to other methods.

Optimization of semi-supervised generative adversarial network models: a survey

PurposeWith the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.Design/methodology/approachTo address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.FindingsFollowing the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.Originality/valueThis paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

ML-Net: A Multi-Local Perception Network for Healthy and Bleached Coral Image Classification

ML-Net: A Multi-Local Perception Network for Healthy and Bleached Coral Image Classification

MultiFusionNet: multilayer multimodal fusion of deep neural networks for chest X-ray image classification

MultiFusionNet: multilayer multimodal fusion of deep neural networks for chest X-ray image classification

Global-local manifold embedding broad graph convolutional network for hyperspectral image classification

Graph convolutional neural networks (GCNs) with domain-specific feature aggregation capabilities have unique advantages in hyperspectral image (HSI) classification. However, current GCN-based approaches frequently encounter the issue of node characteristics being over-smoothed while aggregating in higher-order domains. Furthermore, GCN linear classifiers focus solely on sample separability and ignore the potential manifold information of graph features, resulting in a failure to fully investigate extracted features. To address these problems, we propose a global-local manifold embedding broad graph convolutional network (GLMBG) for HSI classification. In GLMBG, we designed two modules from feature extraction and classification perspectives: The graph convolutional edge feature fusion extractor (GEFF) and the broad classifier of global-local manifold embedding (BGLME). GEFF is designed to learn graph node and local edge features from HSI through GCN and recursive filtering, combining them in a weighted manner to construct fused graph features. BGLME is designed to replace traditional linear classifiers with broad learning classifiers through manifold regularized embedding, fully utilizing the global and local manifold discriminant information of graph node features. The combination of GEFF and BGLME effectively reduces over-smoothing of graph node features while maximizing the utilization of manifold discriminant information, hence improving model feature discriminative ability. Experimental evaluations of three commonly used hyperspectral datasets show that our method surpasses state-of-the-art methods.

Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with a high incidence and mortality rate in intensive care unit (ICU) admissions. Early identification of patients at high risk for developing ARDS is crucial for timely intervention and improved clinical outcomes. However, the complex pathophysiology of ARDS makes early prediction challenging. This study aimed to develop an artificial intelligence (AI) model for automated lung lesion segmentation and early prediction of ARDS to facilitate timely intervention in the intensive care unit. A total of 928 ICU patients with chest computed tomography (CT) scans were included from November 2018 to November 2021at three centers in China. Patients were divided into a retrospective cohort for model development and internal validation, and three independent cohorts for external validation. A deep learning-based framework using the UNet Transformer (UNETR) model was developed to perform the segmentation of lung lesions and early prediction of ARDS. We employed various data augmentation techniques using the Medical Open Network for AI (MONAI) framework, enhancing the training sample diversity and improving the model's generalization capabilities. The performance of the deep learning-based framework was compared with a Densenet-based image classification network and evaluated in external and prospective validation cohorts. The segmentation performance was assessed using the Dice coefficient (DC), and the prediction performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The contributions of different features to ARDS prediction were visualized using Shapley Explanation Plots. This study was registered with the China Clinical Trial Registration Centre (ChiCTR2200058700). The segmentation task using the deep learning framework achieved a DC of 0.734±0.137 in the validation set. For the prediction task, the deep learning-based framework achieved AUCs of 0.916 [0.858-0.961], 0.865 [0.774-0.945], 0.901 [0.835-0.955], and 0.876 [0.804-0.936] in the internal validation cohort, external validation cohort I, external validation cohort II, and prospective validation cohort, respectively. It outperformed the Densenet-based image classification network in terms of prediction accuracy. Moreover, the ARDS prediction model identified lung lesion features and clinical parameters such as C-reactive protein, albumin, bilirubin, platelet count, and age as significant contributors to ARDS prediction. The deep learning-based framework using the UNETR model demonstrated high accuracy and robustness in lung lesion segmentation and early ARDS prediction, and had good generalization ability and clinical applicability. This study was supported by grants from the Shanghai Renji Hospital Clinical Research Innovation and Cultivation Fund (RJPY-DZX-008) and Shanghai Science and Technology Development Funds (22YF1423300).

A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification.

Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational demands of a graph convolution network (GCN) present challenges for real-time applications. To overcome these issues, a dual-branch fusion of a GCN and convolutional neural network (DFGCN) is proposed for HSIC tasks. The GCN branch uses an adaptive multi-scale superpixel segmentation method to build fusion adjacency matrices at various scales, which improves the graph convolution efficiency and node representations. Additionally, a spectral feature enhancement module (SFEM) enhances the transmission of crucial channel information between the two graph convolutions. Meanwhile, the CNN branch uses a convolutional network with an attention mechanism to focus on detailed features of local areas. By combining the multi-scale superpixel features from the GCN branch and the local pixel features from the CNN branch, this method leverages complementary features to fully learn rich spatial-spectral information. Our experimental results demonstrate that the proposed method outperforms existing advanced approaches in terms of classification efficiency and accuracy across three benchmark data sets.