Image Classification Research Articles

Hyperspectral Image Analysis Using Cloud-Based Support Vector Machines

Hyperspectral image processing techniques involve time-consuming calculations due to the large volume and complexity of the data. Indeed, hyperspectral scenes contain a wealth of spatial and spectral information thanks to the hundreds of narrow and continuous bands collected across the electromagnetic spectrum. Predictive models, particularly supervised machine learning classifiers, take advantage of this information to predict the pixel categories of images through a training set of real observations. Most notably, the Support Vector Machine (SVM) has demonstrate impressive accuracy results for image classification. Notwithstanding the performance offered by SVMs, dealing with such a large volume of data is computationally challenging. In this paper, a scalable and high-performance cloud-based approach for distributed training of SVM is proposed. The proposal address the overwhelming amount of remote sensing (RS) data information through a parallel training allocation. The implementation is performed over a memory-efficient Apache Spark distributed environment. Experiments are performed on a benchmark of real hyperspectral scenes to show the robustness of the proposal. Obtained results demonstrate efficient classification whilst optimising data processing in terms of training times.

Deep learning-based fully automatic Risser stage assessment model using abdominal radiographs.

Artificial intelligence has been increasingly used in medical imaging and has demonstrated expert level performance in image classification tasks. To develop a fully automatic approach for determining the Risser stage using deep learning on abdominal radiographs. In this multicenter study, 1,681 supine abdominal radiographs (age range, 9-18years, 50% female) obtained between January 2019 and April 2022 were collected retrospectively from three medical institutions and graded manually using the United States Risser staging system. A total of 1,577 images from Hospitals 1 and 2 were used for development, and 104 images from Hospital 3 for external validation. From each radiograph, right and left iliac crest patch images were extracted using the pelvic bone segmentation model DeepLabv3 + with the EfficientNet-B0 encoder trained with 90 digitally reconstructed radiographs from pelvic computed tomography scans with a pelvic bone mask. Using these patch images, ConvNeXt-B was trained to grade according to the Risser classification. The model's performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUROC), and mean absolute error. The fully automatic Risser stage assessment model showed an accuracy of 0.87 and 0.75, mean absolute error of 0.13 and 0.26, and AUROC of 0.99 and 0.95 on internal and external test sets, respectively. We developed a deep learning-based, fully automatic segmentation and classification model for Risser stage assessment using abdominal radiographs.

Machine learning for automating subjective clinical assessment of gait impairment in people with acquired brain injury – a comparison of an image extraction and classification system to expert scoring

BackgroundWalking impairment is a common disability post acquired brain injury (ABI), with visually evident arm movement abnormality identified as negatively impacting a multitude of psychological factors. The International Classification of Functioning, Disability and Health (ICF) qualifiers scale has been used to subjectively assess arm movement abnormality, showing strong intra-rater and test-retest reliability, however, only moderate inter-rater reliability. This impacts clinical utility, limiting its use as a measurement tool. To both automate the analysis and overcome these errors, the primary aim of this study was to evaluate the ability of a novel two-level machine learning model to assess arm movement abnormality during walking in people with ABI.MethodsFrontal plane gait videos were used to train four networks with 50%, 75%, 90%, and 100% of participants (ABI: n = 42, healthy controls: n = 34) to automatically identify anatomical landmarks using DeepLabCut™ and calculate two-dimensional kinematic joint angles. Assessment scores from three experienced neurorehabilitation clinicians were used with these joint angles to train random forest networks with nested cross-validation to predict assessor scores for all videos. Agreement between unseen participant (i.e. test group participants that were not used to train the model) predictions and each individual assessor’s scores were compared using quadratic weighted kappa. One sample t-tests (to determine over/underprediction against clinician ratings) and one-way ANOVA (to determine differences between networks) were applied to the four networks.ResultsThe machine learning predictions have similar agreement to experienced human assessors, with no statistically significant (p < 0.05) difference for any match contingency. There was no statistically significant difference between the predictions from the four networks (F = 0.119; p = 0.949). The four networks did however under-predict scores with small effect sizes (p range = 0.007 to 0.040; Cohen’s d range = 0.156 to 0.217).ConclusionsThis study demonstrated that machine learning can perform similarly to experienced clinicians when subjectively assessing arm movement abnormality in people with ABI. The relatively small sample size may have resulted in under-prediction of some scores, albeit with small effect sizes. Studies with larger sample sizes that objectively and automatically assess dynamic movement in both local and telerehabilitation assessments, for example using smartphones and edge-based machine learning, to reduce measurement error and healthcare access inequality are needed.

LCFE-X: Pluggable Local Channel Feature Extraction model enhancement method for hyperspectral image classification

Spectral information of hyperspectral images offers high-dimensional features for downstream tasks. Local spectral signatures in different wavelength ranges are critical for material identification and pixel classification. In line with conventional vision methods, recent studies have focused on bolstering the spatial modelling capabilities of transformers. However, the focus remains predominantly global when the channel dimension is considered. Current research on enhancing local channel feature extraction is primarily model specific and lacks generalisable methods that can be applied to various models. Therefore, this study introduces a Local Channel Feature Extraction (LCFE) module. It extracts spectral–spatial features and key features within each band group and then integrates the features to obtain local channel features. Subsequently, a pluggable LCFE-X model enhancement method is proposed. This involves performing LCFE at the front end of the model and fusing the extracted features with the original features. Three enhanced hyperspectral image classification models, namely, LCFE-ViT, LCFE-SF, and LCFE-MF, are constructed based on recently proposed models. Extensive experiments demonstrate that the LCFE module effectively extracts local spectral signatures, and the proposed method enhances the performance of the various basic models. The code is available at https://github.com/YiSu1997/LCFE-X.

The artistic image processing for visual healing in smart city

This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents’ mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives.

Leveraging camera traps and artificial intelligence to explore thermoregulation behaviour.

Behavioural thermoregulation has critical ecological and physiological consequences that profoundly influence individual fitness and species distributions, particularly in the context of climate change. However, field monitoring of this behaviour remains labour-intensive and time-consuming. With the rise of camera-based surveys and artificial intelligence (AI) approaches in computer vision, we should try to build better tools for characterizing animals' behavioural thermoregulation. In this study, we developed a deep learning framework to automate the detection and classification of thermoregulation behaviour. We used lizards, the Rough-tail rock agama (Laudakia vulgaris), as a model animal for thermoregulation. We colour-marked the lizards and curated a diverse dataset of images captured by trail cameras under semi-natural conditions. Subsequently, we trained an object-detection model to identify lizards and image classification models to determine their microclimate usage (activity in sun or shade), which may indicate thermoregulation preferences. We then evaluated the performance of each model and analysed how the classification of thermoregulating lizards performed under different solar conditions (sun or shade), times of day and marking colours. Our framework's models achieved high scores in several performance metrics. The behavioural thermoregulation classification model performed significantly better on sun-basking lizards, achieving the highest classification accuracy with white-marked lizards. Moreover, the hours of activity and the microclimate choices (sun vs shade-seeking behaviour) of lizards, generated by our framework, are closely aligned with manually annotated data. Our study underscores the potential of AI in effectively tracking behavioural thermoregulation, offering a promising new direction for camera trap studies. This approach can potentially reduce the labour and time associated with ecological data collection and analysis and help gain a deeper understanding of species' thermal preferences and risks of climate change on species behaviour.

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.

Deep feature dendrite with weak mapping for small-sample hyperspectral image classification

Deep feature dendrite with weak mapping for small-sample hyperspectral image classification

An attention-based neural network for lung cancer classification and gradient in MRI

Accurate lung cancer classification in magnetic resonance imaging (MRI) remains challenging due to the difficulty in detecting cancerous patterns. In response, this study introduces an attention-based VGG19 neural network for enhanced classification performance. Leveraging the VGG19 architecture's deep learning capabilities, our model incorporates attention mechanisms to selectively emphasize salient features during training. The attention-based approach addresses the challenge of discerning subtle patterns indicative of malignancy, significantly improving classification accuracy. We train and evaluate the model on a diverse dataset, ensuring its capacity to generalize across various patient cases. The attention mechanism proves effective in prioritizing critical regions within MRI scans, enhancing sensitivity and specificity in lung cancer detection. Additionally, we employ gradient analysis to interpret the decision-making process, providing valuable insights into influential features. Results demonstrate the proposed model's superiority over baseline approaches, showcasing its efficacy in inaccurate lung cancer classification. The attention-based VGG19 neural network not only advances classification capabilities but also offers interpretability crucial for gaining trust in automated diagnostic systems. This research contributes a robust solution to a pressing medical imaging challenge, holding promise for practical implementation in clinical settings to support radiologists in timely and accurate lung cancer diagnosis.

A Joint Network of Edge-Aware and Spectral–Spatial Feature Learning for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a vital part of the HSI application field. Since HSIs contain rich spectral information, it is a major challenge to effectively extract deep representation features. In existing methods, although edge data augmentation is used to strengthen the edge representation, a large amount of high-frequency noise is also introduced at the edges. In addition, the importance of different spectra for classification decisions has not been emphasized. Responding to the above challenges, we propose an edge-aware and spectral–spatial feature learning network (ESSN). ESSN contains an edge feature augment block and a spectral–spatial feature extraction block. Firstly, in the edge feature augment block, the edges of the image are sensed, and the edge features of different spectral bands are adaptively strengthened. Then, in the spectral–spatial feature extraction block, the weights of different spectra are adaptively adjusted, and more comprehensive depth representation features are extracted on this basis. Extensive experiments on three publicly available hyperspectral datasets have been conducted, and the experimental results indicate that the proposed method has higher accuracy and immunity to interference compared to state-of-the-art (SOTA) method.

Gradually Vanishing Bridge Based on Multi-Kernel Maximum Mean Discrepancy for Breast Ultrasound Image Classification

Gradually Vanishing Bridge Based on Multi-Kernel Maximum Mean Discrepancy for Breast Ultrasound Image Classification

Transformer-based land use and land cover classification with explainability using satellite imagery

Transformer-based models have greatly improved Land Use and Land Cover (LULC) applications. Their revolutionary ability to analyze and extract key information has greatly advanced the field. However, the high computational cost of these models presents a considerable obstacle to their practical implementation. Therefore, this study aims to strike a balance between computational cost and accuracy when employing transformer-based models for LULC analysis. We exploit transfer learning and fine-tuning strategies to optimize the resource utilization of transformer-based models. Furthermore, transparency is the core principle of our methodology to promote fairness and trust in applying LULC models across various domains, including forestry, environmental studies, and urban or rural planning. To ensure transparency, we have employed Captum, which enables us to uncover and mitigate potential biases and interpret AI-driven decisions. Our results indicate that transfer learning can potentially improve transformer-based models in satellite image classification, and strategic fine-tuning can maintain efficiency with minimal accuracy trade-offs. This research highlights the potential of Explainable AI (XAI) in Transformer-based models for achieving more efficient and transparent LULC analysis, thereby encouraging continued innovation in the field.

Fault transformer: An automatic fault detection algorithm on seismic images using a transformer enhanced neural network

Seismic fault detection is a key step in seismic interpretation and reservoir characterization that often requires a large amount of human labor and interpretation time. Therefore, automatic seismic fault detection is critical for improving the efficiency of seismic data processing and interpretation. Existing artificial intelligence methods are mostly based on convolutional neural networks with a U-shaped encoder-decoder structure, known as U-net. However, the convolution is limited in modeling long-range correlative features. Instead, transformers, using self-attention mechanisms, avoid the local nature of the convolution, which has the potential to extract long-distance correlations. Transformers are proven to perform well in natural language processing, image classification, and segmentation tasks in precision and recall. Here, we develop a new deep neural network with transformers and a U-net-like structure: a fault transformer to perform the fault detection task. The new network outperforms the traditional U-net in the application with synthetic data sets.

Image Classification of Skin Cancer Using Deep Neural Networks with Scaling Laws

Image Classification of Skin Cancer Using Deep Neural Networks with Scaling Laws

Learning generalizable AI models for multi-center histopathology image classification

Investigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use. Several techniques, including the use of grayscale images, color normalization techniques, and Adversarial Domain Adaptation (ADA) have been suggested to generalize deep learning algorithms, but there are limitations to their effectiveness and discriminability. Convolutional Neural Networks (CNNs) exhibit higher sensitivity to variations in the amplitude spectrum, whereas humans predominantly rely on phase-related components for object recognition. As such, we propose Adversarial fourIer-based Domain Adaptation (AIDA) which applies the advantages of a Fourier transform in adversarial domain adaptation. We conducted a comprehensive examination of subtype classification tasks in four cancers, incorporating cases from multiple medical centers. Specifically, the datasets included multi-center data for 1113 ovarian cancer cases, 247 pleural cancer cases, 422 bladder cancer cases, and 482 breast cancer cases. Our proposed approach significantly improved performance, achieving superior classification results in the target domain, surpassing the baseline, color augmentation and normalization techniques, and ADA. Furthermore, extensive pathologist reviews suggested that our proposed approach, AIDA, successfully identifies known histotype-specific features. This superior performance highlights AIDA’s potential in addressing generalization challenges in deep learning models for multi-center histopathology datasets.

Retraction Note: Hybrid optimization technique to improve feature selection in image classification technique using RBFNN and ABC

Retraction Note: Hybrid optimization technique to improve feature selection in image classification technique using RBFNN and ABC

Permutation mask: a combined gradient sparsification for federated learning

Large-scale distributed model training suffers from communication overheads due to frequent gradient updates transmitted between compute nodes. This situation gets even worse with training on mobile devices, which suffers from high latency, lower throughput and intermittent poor connections. Various recent works proposed to use quantisation or sparsification techniques to reduce the amount of data that need to be communicated, for instance Top-k sparsification. However, the Top-k sparsification is lack of the cooperation between nodes. In this paper, we present the permutation mask (Pmask), a novel gradient sparsification technique for distributed model training. To unite nodes and improve the convergence rate, Pmask employs non-zero average and combined gradients. The corresponding convergence property is also given. We have applied permutation mask to image classification task on Cifar10, mini-ImageNet and SVHN, and the experiments show that Pmask leads to a faster distributed training and improves the accuracy.

Research on ischemic stroke mri image classification based on convolutional neural network

Research on ischemic stroke mri image classification based on convolutional neural network

SPLICE - Streamlining Digital Pathology Image Processing

Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whole Slide Images (WSIs), there’s a growing demand for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this paper, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a “collage” of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting nonredundant representative features. We evaluated SPLICE for search and match applications, demonstrating improved accuracy, reduced computation time, and storage requirements compared to existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduces storage requirements for representing tissue images by 50%. This reduction enables numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.

HyperGCN – a multi-layer multi-exit graph neural network to enhance hyperspectral image classification

ABSTRACT Graph neural networks (GNNs) have recently garnered significant attention due to their exceptional performance across various applications, including hyperspectral (HS) image classification. However, most existing GNN-based models for HS image classification are limited depth models and often suffer from performance degradation as model depth increases. This study introduces HyperGCN, an exclusive GNN-based model designed with multiple graph convolutional layers to exploit the rich spectral information inherent in HS images, thereby enhancing classification performance. To address performance degradation, HyperGCN incorporates techniques resistant to oversmoothing into its architecture. Additionally, multiple-side exit branches are integrated into the intermediate layers of HyperGCN, enabling dynamic management of the complexity of HS images. Less complex HS images are processed by fewer layers, exiting early via attached branches, while more complex images traverse multiple layers until reaching the final output layer. Extensive experiments on four benchmark HS datasets (Indian Pines, Pavia University, Salinas, and Botswana) demonstrate HyperGCN’s superior performance over basic GNN-based models. Notably, HyperGCN outperforms or performs comparably to the CNN-GNN combined model in classifying HS images. Furthermore, the superior performance of multi-exit HyperGCN over its single-exit counterpart emphasizes the effectiveness of incorporating side exit branches in GNN-based HS image classification. Compared to state-of-the-art models, multi-exit HyperGCN demonstrates competitive performance, highlighting its effectiveness in handling complex spectral information in HS images while maintaining an acceptable balance between accuracy and computational efficiency.