Networks For Image Classification Research Articles

LCTCS: Low-Cost and Two-Channel Sparse Network for Hyperspectral Image Classification

Using convolutional neural networks (CNNs) in classifying hyperspectral images (HSIs) has achieved quite good results in recent years. It is widely used in agricultural remote sensing, geological exploration, environmental monitoring, and marine remote sensing. Unfortunately, the complexity of network structures used for hyperspectral image classification challenges the efficient delivery of HSI data extremely, and existing methods suffer from a large amount of redundancy in the network weight parameters during training, as they either require huge computational resources or make inefficient use of storage space when designing the network structure, and many of the parameters that waste computational resources contribute less to the rich spectral and spatial information transfer in HSI. So we introduce LCTCS, a better low-memory and less-parametric network approach. LCTCS aims to improve the efficiency of computational resource utilization with advanced classification performance and lower levels of computational resources. Unlike the conventional 2D and 3D convolution used previously, we use simple and efficient 3D grouped convolution as a vehicle to convey the semantic features of HSIs. More specifically, we design a novel two-channel sparse network to classify HSIs since grouped 3D convolution conveys the properties of hyperspectral data well in the time and space domains.We have compared LCTCS with eight widely used network methods on four publicly available hyperspectral datasets for learning HSI information. A series of experiments shows that the model architecture designed has 65.89%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$65.89 \\%$$\\end{document} less storage space than the DBDA method, consumes 67.36%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$67.36 \\%$$\\end{document} fewer computational resources than the SSRN method on the IP dataset, and accomplishes a highly accurate classification task with the number of parameters accounting for only 1.99%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.99 \\%$$\\end{document} that of the DBMA method.

Deep Learning-Based Computer-Aided Diagnosis of Osteochondritis Dissecans of the Humeral Capitellum Using Ultrasound Images.

Ultrasonography is used to diagnose osteochondritis dissecans (OCD) of the humerus; however, its reliability depends on the technical proficiency of the examiner. Recently, computer-aided diagnosis (CAD) using deep learning has been applied in the field of medical science, and high diagnostic accuracy has been reported. We aimed to develop a deep learning-based CAD system for OCD detection on ultrasound images and to evaluate the accuracy of OCD detection using the CAD system. The CAD process comprises 2 steps: humeral capitellum detection using an object-detection algorithm and OCD classification using an image classification network. Four-directional ultrasound images of the elbow of the throwing arm of 196 baseball players (mean age, 11.2 years), including 104 players with normal findings and 92 with OCD, were used for training and validation. An external dataset of 20 baseball players (10 with normal findings and 10 with OCD) was used to evaluate the accuracy of the CAD system. A confusion matrix and the area under the receiver operating characteristic curve (AUC) were used to evaluate the system. Clinical evaluation using the external dataset resulted in high AUCs in all 4 directions: 0.969 for the anterior long axis, 0.966 for the anterior short axis, 0.996 for the posterior long axis, and 0.993 for the posterior short axis. The accuracy of OCD detection thus exceeded 0.9 in all 4 directions. We propose a deep learning-based CAD system to detect OCD lesions on ultrasound images. The CAD system achieved high accuracy in all 4 directions of the elbow. This CAD system with a deep learning model may be useful for OCD screening during medical checkups to reduce the probability of missing an OCD lesion. Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Quantum convolutional neural networks for multiclass image classification

Quantum convolutional neural networks for multiclass image classification

INSURANCE AMOUNT PREDICTION BASED ON ACCIDENTAL CAR DAMAGE LEVEL USING CNN

There is a paradigm shift in the automotive industry with the advancement of technology, especially in the area of ​​insurance claims handling. In this study, we propose a new method for predicting insurance claims based on car damage levels, utilizing advanced artificial intelligence algorithms for image analysis, particularly employing Convolutional Neural Networks (CNN) for image processing and classification. Our strategy is to provide car damage estimates through picture analysis, making insurance claims in the automobile sector simpler. In order to facilitate user interaction with Flask and increase the predictability of our models, we have also implemented enhancements to the user interface. By combining image analysis with a user-friendly interface, our method offers a practical solution for insurance companies to expedite claim processing, reduce fraudulent activities, and ensure fair settlements for policyholders. This research contributes to the ongoing transformation of insurance claim processing within the automotive industry, highlighting the potential of merging image analysis methodologies with user interfaces for enhanced efficiency and usability. Keywords: Insurance claim prediction, Convolutional Neural Networks (CNNs), Image analysis, User interface, Flask, Automotive industry.

Multi-hop double-branch network for small sample hyperspectral image classification

ABSTRACT Neural networks have been extensively utilized in the classification of hyperspectral images. The study introduces a novel dual-branch structured network named MGACN for hyperspectral image classification under the condition of small sample sizes. This model a multi-hop graph network (MHGCN) and a graph attention convolutional network (GACNN). Graph Convolutional Network (GCN) integrates multi-hop mechanism to obtain and aggregate information between long-distance non-neighbor nodes. The Graph Attention Network (GAT) combines 2D-CNN by incorporating a noise reduction mechanism, as well as position and channel attention mechanisms. The classification accuracy reaches 98.43%, 99.02%, and 98.55% on the Indian Pines, Salinas, and Pavia University datasets, which is significantly better than that of the comparison model.

GACDNet:Mapping winter wheat by generative adversarial cross-domain networks with transformer integration for zero-sample extraction

Accurate extraction of winter wheat area is essential for wheat yield estimation. Remotely sensed images have limited coverage, are taken at different times, from different angles and in different geographical areas, and the spectral information of the same feature varies from image to image. Machine learning methods are very popular in extracting winter wheat planting area, there are differences between different images, namely the distribution differences between source domain data and target domain data, the results obtained by these methods directly applied to other areas are not satisfactory. To achieve cross regional extraction of winter wheat area, we propose generative adversarial cross-domain networks for image classification under the same image type. The cross-domain network proposed comprises a generative network and a feature extractor. The generative network creates diverse and acceptable samples from the initial input data, constrained by the contrast loss. The feature extractor converts the initial input data and the generated data into a high-level representation. Two time series datasets were constructed and a series of experiments were conducted on these datasets, obtaining high-precision and optimal classification results. Sample training was performed using 70%, 50%, 30% and 10% of the data on both datasets. The study results demonstrated the high performance and cross-domain capability of our network. Furthermore, the winter wheat cultivation area of the entire Zhoukou city was extracted, yielding results with an accuracy of 94.23%. Extensive experiments and applications on the dataset demonstrate that our method is feasible and reliable.

MBSI-Net: Multimodal Balanced Self-Learning Interaction Network for Image Classification

MBSI-Net: Multimodal Balanced Self-Learning Interaction Network for Image Classification

A novel multi-task learning network for skin lesion classification based on multi-modal clues and label-level fusion

In this paper, we propose a multi-task learning (MTL) network based on the label-level fusion of metadata and hand-crafted features by unsupervised clustering to generate new clustering labels as an optimization goal. We propose a MTL module (MTLM) that incorporates an attention mechanism to enable the model to learn more integrated, variable information. We propose a dynamic strategy to adjust the loss weights of different tasks, and trade off the contributions of multiple branches. Instead of feature-level fusion, we propose label-level fusion and combine the results of our proposed MTLM with the results of the image classification network to achieve better lesion prediction on multiple dermatological datasets. We verify the effectiveness of the proposed model by quantitative and qualitative measures. The MTL network using multi-modal clues and label-level fusion can yield the significant performance improvement for skin lesion classification.

An efficient Fusion-Purification Network for Cervical pap-smear image classification

Background and Objectives:In cervical cell diagnostics, autonomous screening technology constitutes the foundation of automated diagnostic systems. Currently, numerous deep learning-based classification techniques have been successfully implemented in the analysis of cervical cell images, yielding favorable outcomes. Nevertheless, efficient discrimination of cervical cells continues to be challenging due to large intra-class and small inter-class variations. The key to dealing with this problem is to capture localized informative differences from cervical cell images and to represent discriminative features efficiently. Existing methods neglect the importance of global morphological information, resulting in inadequate feature representation capability. Methods:To address this limitation, we propose a novel cervical cell classification model that focuses on purified fusion information. Specifically, we first integrate the detailed texture information and morphological structure features, named cervical pathology information fusion. Second, in order to enhance the discrimination of cervical cell features and address the data redundancy and bias inherent after fusion, we design a cervical purification bottleneck module. This model strikes a balance between leveraging purified features and facilitating high-efficiency discrimination. Furthermore, we intend to unveil a more intricate cervical cell dataset: Cervical Cytopathology Image Dataset (CCID). Results:Extensive experiments on two real-world datasets show that our proposed model outperforms state-of-the-art cervical cell classification models. Conclusions:The results show that our method can well help pathologists to accurately evaluate cervical smears.

An Analysis of Convolutional Neutral Network for Image Classification

Abstract: Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image classification tasks, owing to their ability to automatically learn hierarchical representations from raw pixel data. This paper presents a comprehensive analysis of CNNs for image classification, focusing on their architecture, training process, and performance evaluation metrics. The study investigates various CNN architectures, including AlexNet, GoogLeNet, and ResNet, highlighting their respective strengths and weaknesses in different scenarios. Moreover, the paper explores the impact of hyperparameters such as network depth, filter size, and pooling strategies on classification accuracy and computational efficiency. Furthermore, the training process of CNNs, encompassing data preprocessing, augmentation techniques, and optimization algorithms, is scrutinized to elucidate best practices for achieving optimal performance. Additionally, the paper discusses commonly used evaluation metrics such as accuracy, precision, recall, and F1-score, elucidating their interpretation and significance in assessing model performance. Through a systematic review of existing literature and experimental validation, this analysis aims to provide insights into the underlying mechanisms of CNNs for image classification tasks. Finally, the paper outlines future research directions, emphasizing the need for exploring novel architectures, optimizing hyperparameters, and enhancing interpretability and robustness of CNN models. Overall, this study contributes to a deeper understanding of CNNs for image classification and provides valuable guidance for practitioners and researchers in the field.

Feature disentanglement based domain adaptation network for cross-scene coastal wetland hyperspectral image classification

At present, domain adaptation (DA) methods have made noteworthy advancements in cross-scene hyperspectral image (HSI) classification. Their success largely hinges on the alignment of distributions between source and target domains, which is a critical step in extracting domain-invariant features. However, this intense focus on domain-invariant feature extraction frequently leads to the neglect of class-discriminative features, limiting their utility in cross-scene coastal wetland classification, where the nuanced identification of different classes is crucial. In this paper, a feature disentanglement based domain adaptation network (FDDAN) is proposed to disentangle and exclude domain-specific features and class-invariant features, thereby obtaining class-specific domain-invariant features for classification tasks. Specifically, a transformer and convolution fusion-based feature extraction network is designed to capture global–local mixed features. To align domain distributions and learn shared features, two corresponding disentanglers separate domain-invariant features and domain-specific features from mixed features. Furthermore, to allow domain-invariant features containing purer category discriminative information, class-invariant features are also segregated. In addition, an adversarial learning strategy between three features is utilized to simultaneously enhance the transferability and discriminability of domain-invariant features. The effectiveness of FDDAN is demonstrated by the experimental results obtained from three cross-scene unmanned aerial vehicle datasets collected in coastal wetlands and a publicly available dataset. We will make our datasets and source code publicly available upon acceptance of the paper.

Review of Convolutional Neural Network Models and Image Classification

With the arrival of the era of big data and the improvement of computing power, deep learning has swept the world. Traditional image classification methods are difficult to deal with the huge image data, and cannot meet the requirements of people on the accuracy and speed of image classification, the image classification method based on convolutional neural network breaks through the bottleneck of the traditional image classification method, and becomes the mainstream algorithm of image classification, how to effectively use convolutional neural network to classify images has become a hot spot of research in the field of computer vision at home and abroad. In this paper, we review the research background, significance and current research status of convolutional neural network model and image classification, study two image classification methods based on ResNet and ShuffleNet, and provide a comprehensive review of the construction methods and characteristics of the two deep convolutional neural network model structures, and finally compare and analyse the performance of the two classification models.

Machine Learning for Depression Detection on Web and Social Media

Depression, a significant psychiatric disorder, affects individuals' physical well-being and daily functioning. This focused analysis provides a comprehensive exploration of contemporary research conducted between 2012 and 2023 that delves into the utilization of sophisticated machine learning methodologies aimed at identifying correlates of depression within social media content. Our study meticulously dissects various data sources and performs a comprehensive examination of different machine learning algorithms cited in the researched articles and literature, aiming to pinpoint an approach that can enhance detection accuracy. Furthermore, we have scrutinized the use of varied data from social media platforms and pinpointed emerging trends, notably spotlighting novel applications of artificial neural networks for image processing and classification, along with advanced gait image models. Our results offer essential direction for future research focused on enhancing detection precision, acting as a valuable reference for academic and industry scholars in this field.

PathNet: a novel multi-pathway convolutional neural network for few-shot image classification from scratch

PathNet: a novel multi-pathway convolutional neural network for few-shot image classification from scratch

Automatic reconstruction of closely packed fabric composite RVEs using yarn-level micro-CT images processed by convolutional neural networks (CNNs) and based on physical characteristics

Micro-CT scanning is an advanced technique to reconstruct inner architectures for RVEs of fabric composites. Currently, however, there exist few automatic approaches to separate closely packed yarns in the micro-CT images with economical resolution that can only identify yarns but not individual fibers. To tackle this issue, an innovative method has been developed in this work to identify cross-section area of the yarns via deep-learning-based image segmentation, and then reconstruct 3D geometry model of the composites containing local fiber orientations. The image segmentation was achieved through semantic segmentation by U-Net with validation accuracy of 87 % counted by mIOU and object detection by YOLO v8 with validation accuracy of 99.5 % counted by mAP50. Micro-CT slices with different morphological characteristics were divided into three groups via a ResNet50-based image classification network and selected in ratio to form the training datasets for U-Net and YOLO v8 with high representativity and efficiency. Extraction of individual cross-sections of weft and warp yarns were conducted only using the micro-CT scanning from one angle of view to reduce scanning cost and yarn-to-yarn penetration error. An algorithm considering physical constraints of the yarns was also completed to estimate local fiber orientations with maximum error of 18.065°, nearly 50 % smaller than the existing method.

Exploring deep echo state networks for image classification: a multi-reservoir approach

Exploring deep echo state networks for image classification: a multi-reservoir approach

ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry.

Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains. In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks. Through extensive ablation studies, we show that we can surpass previous benchmarks of margin evaluation in BCC surgery using iKnife data, achieving an area under the receiver operating characteristic curve (AUC) of 81%. We also depict the attention maps of the developed DL models to evaluate the biological relevance of the embedding space CONCLUSIONS: We propose a new method for characterizing tissue at the surgical margins, using mass spectrometry data from cancer surgery.

Evaluation of Shallow Convolutional Neural Network in Open-World Chart Image Classification

Evaluation of Shallow Convolutional Neural Network in Open-World Chart Image Classification

Attention 3D central difference convolutional dense network for hyperspectral image classification.

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

Residual cosine similar attention and bidirectional convolution in dual-branch network for skin lesion image classification

Skin cancer is one of the most serious threats to human health among skin lesions. Computer-aided diagnosis methods can assist patients in identifying and detecting skin lesion types early, thereby enabling corresponding treatments. In this paper, we propose a dual-branch neural network model Conformer with Residual Cosine Similarity Attention and Bidirectional Convolutional fusion strategy, named RCSABC-Conformer. The core of this network structure comprises three parts: a Convolutional Neural Network (CNN) branch with Residual Cosine Similarity Attention (RCSA), a Transformer branch, and a Feature Couple Unit with Bidirectional Convolutional strategy (BC-FCU). The RCSA module calculates the cosine similarity value between the feature map generated by the convolutional operation and the feature map of the residual edge to assess whether their semantic information is similar. The semantic information of similar parts is weighted by exponential normalization to enhance the network's memory of similar features of the same type of skin lesion. The BC-FCU module interactively fuses local features and global representations of skin lesion images with different resolutions in the two branches. Specifically, when the global representations is integrated into local features, we introduce a new bidirectional convolution strategy to extract the feature map from both forward and backward directions, and then select the element with the smaller feature value from the two directions to fuse into local features. In this way, we can minimize the interference of the artifact features extracted by the Transformer branch on the CNN branch. In addition, taking advantage of the Transformer branch's capacity to construct global representations, our model can learn contextual semantic information of normal skin and lesion areas to enhance model robustness. We conducted experiments on three datasets, consisting of clinical and dermoscopic skin lesion images, as well as a hybrid of both. The experimental results show that RCSABC-Conformer outperforms both advanced and classical classification methods in terms of classification accuracy across all three datasets, without requiring an increase in the number of parameters and computational complexity. Compared with the baseline model, the classification accuracy of our proposed method improves by 2.40%, 5.39%, and 4.44% on the three datasets, respectively. To the best of our knowledge, this is the first study to apply an interactive fusion dual-branch network for multi-disease classification on different modalities of skin lesion databases. Code will be available at https://github.com/AlenLi817/RCSABC-Conformer.