Networks For Image Classification Research Articles

C‐TUnet: A CNN‐Transformer Architecture‐Based Ultrasound Breast Image Classification Network

ABSTRACTUltrasound breast image classification plays a crucial role in the early detection of breast cancer, particularly in differentiating benign from malignant lesions. Traditional methods face limitations in feature extraction and global information capture, often resulting in lower accuracy for complex and noisy ultrasound images. This paper introduces a novel ultrasound breast image classification network, C‐TUnet, which combines a convolutional neural network (CNN) with a Transformer architecture. In this model, the CNN module initially extracts key features from ultrasound images, followed by the Transformer module, which captures global context information to enhance classification accuracy. Experimental results demonstrate that the proposed model achieves excellent classification performance on public datasets, showing clear advantages over traditional methods. Our analysis confirms the effectiveness of combining CNN and Transformer modules—a strategy that not only boosts the accuracy and robustness of ultrasound breast image classification but also offers a reliable tool for clinical diagnostics, holding substantial potential for real‐world application.

Uncertainty bidirectional guidance of multi-task mamba network for medical image classification and segmentation

Uncertainty bidirectional guidance of multi-task mamba network for medical image classification and segmentation

Comparison of radiated noise classification methods for underwater targets based on different enhanced images and convolutional neural networks

With the continuous development of economy and society, factors such as the variety of underwater targets and the high level of environmental noise have a great impact on the classification accuracy of underwater target radiation noise, and the traditional classification method based on signal features can no longer meet the requirements of underwater target identification. In this paper, we propose an underwater target radiation noise classification method based on enhanced image and convolutional neural network. First, the underwater target radiation noise signal is converted into enhanced image by various methods, then the converted image data set is used as the input of convolutional neural network for model training, and finally the great advantage of convolutional neural network in image classification is used to accurately classify underwater target radiation noise. In order to propose an optimal augmented image transformation method, this paper uses several augmented image transformation methods and compares the classification results. The experimental results show that the augmented image and convolutional neural network methods based on lagomorphs and corner fields have the highest classification accuracy and the best classification efficiency.

Patch-based hierarchical residual spectral-spatial convolutional network for hyperspectral image classification

Patch-based hierarchical residual spectral-spatial convolutional network for hyperspectral image classification

HyperKAN: Kolmogorov-Arnold Networks Make Hyperspectral Image Classifiers Smarter.

In traditional neural network designs, a multilayer perceptron (MLP) is typically employed as a classification block following the feature extraction stage. However, the Kolmogorov-Arnold Network (KAN) presents a promising alternative to MLP, offering the potential to enhance prediction accuracy. In this paper, we studied KAN-based networks for pixel-wise classification of hyperspectral images. Initially, we compared baseline MLP and KAN networks with varying numbers of neurons in their hidden layers. Subsequently, we replaced the linear, convolutional, and attention layers of traditional neural networks with their KAN-based counterparts. Specifically, six cutting-edge neural networks were modified, including 1D (1DCNN), 2D (2DCNN), and 3D convolutional networks (two different 3DCNNs, NM3DCNN), as well as transformer (SSFTT). Experiments conducted using seven publicly available hyperspectral datasets demonstrated a substantial improvement in classification accuracy across all the networks. The best classification quality was achieved using a KAN-based transformer architecture.

Development of the architecture of a computer aided diagnosis system in medicine

With the development of information technology, automation of various production processes is an urgent task, and medical diagnostics is no exception. In recent decades, artificial intelligence and information technology have been widely used in computer diagnostic systems. However, as technology advances, so do the challenges. Not every system is optimized and fast, and traditional methods are fading into the background. Often, systems do not use cloud technologies and have unoptimized architectures. This all affects their performance and, accordingly, is an urgent problem. The study analyzes the methods used in computer diagnostic systems and compares them in terms of advantages and disadvantages. The scientific works related to computer diagnostic systems in medicine for specific tasks are analyzed. The existing architectures of computer diagnostic systems are analyzed, which made it possible to identify the use of consistent approaches to diagnosis. Based on the analyzed data, the purpose, objectives, object and subject of the study are determined. A new architecture has been developed that uses the capabilities of U-Net for image segmentation and convolutional neural networks for medical image classification. The developed architecture is designed to increase the speed and automation of diagnostic processes through the use of neural networks and, accordingly, reduce human intervention. The scientific novelty of the developed architecture lies in the parallel execution of medical image segmentation and classification tasks, which gives a potential increase in data processing speed, and in the availability of an image generator, which solves the problem of lack of test data for model training.

A Review of Convolutional Neural Networks in Cancer Image Classification

Abstract. Cancer image analysis is a crucial element in the field of modern oncology, offering insights into tumor characteristics that are vital for accurate diagnosis and treatment planning. Conventional diagnostic techniques are frequently constrained by discrepancies in interpretation and delays in processing. In recent years, deep learning techniques, in particular convolutional neural networks (CNNs), have demonstrated remarkable capabilities in processing large-scale medical images, enabling the rapid detection and classification of cancerous tissue with high accuracy and precision. This paper reviews the application of CNNs in cancer image classification, introduces common CNN models, and discusses advances such as transfer learning and multimodal data fusion. Furthermore, this paper examines the current challenges confronting convolutional neural networks (CNNs) in the domain of cancer classification. It proposes promising avenues for future research and underscores the potential of CNNs in enhancing cancer diagnosis and even revolutionizing the healthcare system.

SSBFNet: a spectral-spatial fusion with BiFormer network for hyperspectral image classification

SSBFNet: a spectral-spatial fusion with BiFormer network for hyperspectral image classification

Sa-SNN: spiking attention neural network for image classification

Spiking neural networks (SNNs) are known as third generation neural networks due to their energy efficient and low power consumption. SNNs have received a lot of attention due to their biological plausibility. SNNs are closer to the way biological neural systems work by simulating the transmission of information through discrete spiking signals between neurons. Influenced by the great potential shown by the attention mechanism in convolutional neural networks, Therefore, we propose a Spiking Attention Neural Network (Sa-SNN). The network includes a novel Spiking-Efficient Channel Attention (SECA) module that adopts a local cross-channel interaction strategy without dimensionality reduction, which can be achieved by one-dimensional convolution. It is implemented by convolution, which involves a small number of model parameters but provides a significant performance improvement for the network. The design of local inter-channel interactions through adaptive convolutional kernel sizes, rather than global dependencies, allows the network to focus more on the selection of important features, reduces the impact of redundant features, and improves the network’s recognition and generalisation capabilities. To investigate the effect of this structure on the network, we conducted a series of experiments. Experimental results show that Sa-SNN can perform image classification tasks more accurately. Our network achieved 99.61%, 99.61%, 94.13%, and 99.63% on the MNIST, Fashion-MNIST, N-MNIST datasets, respectively, and Sa-SNN performed well in terms of accuracy compared with mainstream SNNs.

A moment-based pooling approach in convolutional neural networks for breast cancer histopathology image classification

A moment-based pooling approach in convolutional neural networks for breast cancer histopathology image classification

SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by 1.72%, 5.19%, and 1.94% in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods.

Application of convolutional neural networks in image classification and applications of improved convolutional neural networks

Abstract. This paper reviews the application and improvement of convolutional neural networks (CNNs) in image classification. Firstly, a shallow CNN for interstitial lung disease image classification is presented. This model suppresses overfitting through a unique network architecture and optimisation algorithm. Next, the improved VGG16 architecture and MIDNet18 model are discussed and their superior performance in brain tumour image classification is demonstrated. Subsequently, a CNN-CapsNet model for cervical cancer image classification and its improvement are presented and the customised model is compared with the conventional VGG-16 CNN architecture in the paper. Next, the application of sparse convolutional kernels and hybrid sparse convolutional kernels (HDCs) in solving the problem of computational resource consumption is presented. Subsequently, methods for solving the problem of limited training data through transfer learning and network data augmentation techniques are discussed, as well as GAN-generated datasets for solving the overfitting problem. Finally, the effect of degraded images on the classification effectiveness of CNNs is explored. The results show that the improved CNN architecture and algorithms have significant effects in solving the problems of overfitting and computational resource consumption, and can significantly improve the accuracy and efficiency of image classification. And degraded images do adversely affect the accuracy of CNN for image classification.

A general image classification model for agricultural machinery trajectory mode recognition

Field-road trajectory classification is a crucial task for agricultural machinery behavior mode recognition, aiming to distinguish field operation mode and road driving mode automatically. However, the imbalanced distribution of agricultural machine trajectories brings challenges for the field-road trajectory classification task. Additionally, most existing field-road trajectory classification methods have certain shortcomings. For instance, they encounter difficulties in accurately representing the state of agricultural machinery movement using the current features. The data transformation process often leads to information loss, and the model’s generalization capabilities are limited. The performance of the models is constrained by each of these elements. To address these shortcomings, this paper introduces a general image classification model for agricultural machinery trajectory mode recognition named ATRNet. First, to address the issue of imbalanced field-road proportions in agricultural machinery trajectory data, a Conditional Tabular Generative Adversarial Network (CTGAN) is employed to generate quasi trajectories, balancing the distribution of positive and negative samples in the data. This step aims to eliminate biases during the model training process. Second, to accurately characterize the motion status of agricultural machinery, we propose a multiangle feature enhancement method to extract rich spatiotemporal features from trajectory data. Finally, different from conventional field-road trajectory classification models that primarily rely on spatial and temporal information for identifying trajectories, we present a lossless trajectory data representation paradigm. This paradigm maps each trajectory point into a “feature map” and uses an image classification model to capture latent feature representations of trajectory points for the recognition of different behavior modes of agricultural machinery. This paradigm can generalize image classification networks to the field-road trajectory classification task, providing a general vision model solution for agricultural machinery trajectory mode recognition. To validate the effectiveness of the ATRNet model, experiments were conducted on real corn and wheat harvester trajectory datasets. The results demonstrate that the proposed model achieves remarkable performance improvements over the state-of-the-art (SOTA) models. In the corn harvester trajectory dataset, ATRNet achieves an accuracy of 92.36% and an F1-score of 92.34%, surpassing existing SOTA models by 3.12% and 12.46%, respectively. Similarly, in the wheat harvester trajectory dataset, ATRNet achieves an accuracy of 92.36% and an F1-score of 92.33%, outperforming the existing optimal algorithm by 4.76% and 18.18%, respectively.

IncARMAG: A convolutional neural network with multi-level autoregressive moving average graph convolutional processing framework for medical image classification

Effective computer-aided detection and diagnosis algorithms are being sought to carry out complex pattern recognition with great precision while increasing efficiency and robustness through automation. In this study, we leveraged self-constructing graph (SCG) and autoregressive moving average (ARMA) graph convolution to postprocess multi-level feature maps obtained from an Inception V3 network for medical image classification. The adopted SCG learns latent embeddings from the feature maps, which are subsequently used to construct graph topology based on embedding similarity. The ARMA convolution then learns to encode more flexible spectral filter responses that simultaneously takes into account long-range dependencies from the SCG graph. The proposed IncARMAG model was evaluated on 2D MedMNIST datasets, which consist of several imaging modalities and classification tasks in the medical domain. Results from our experiments on MedMNIST datasets showed that IncARMAG outperformed state-of-the-art models on many cases. IncARMAG has also achieved the highest accuracy based on evaluation of various benchmark CNN, transformers, and hybrid models on four selected datasets. These results highlight how IncARMAG is a strong candidate in medical image analysis involving classification tasks.

Advanced Image Classification Using Convolutional Neural Networks

Deep learning is essential for computer vision and object detection. This project explores the use of Convolutional Neural Networks (CNNs) for image classification using the CIFAR-10 dataset, which is widely used and provides a robust benchmark for evaluating image classification models. The CNN model, comprising various convolutional, pooling, and fully connected layers, is trained on a portion of the dataset and tested on another portion to evaluate its performance. Techniques such as data augmentation, dropout, and specific activation functions are employed to enhance the model’s efficacy. The study details the CNN architecture and reports the results, including accuracy metrics, to demonstrate the model’s effectiveness.

PBNet: Combining Transformer and CNN in Passport Background Texture Printing Image Classification

Passport background texture classification has always been an important task in border checks. Current manual methods struggle to achieve satisfactory results in terms of consistency and stability for weakly textured background images. For this reason, this study designs and develops a CNN and Transformer complementary network (PBNet) for passport background texture image classification. We first design two encoders by Transformer and CNN to produce complementary features in the Transformer and CNN domains, respectively. Then, we cross-wisely concatenate these complementary features to propose a feature enhancement module (FEM) for effectively blending them. In addition, we introduce focal loss to relieve the overfitting problem caused by data imbalance. Experimental results show that our PBNet significantly surpasses the state-of-the-art image segmentation models based on CNNs, Transformers, and even Transformer and CNN combined models designed for passport background texture image classification.

MHA-DGCLN: multi-head attention-driven dynamic graph convolutional lightweight network for multi-label image classification of kitchen waste

MHA-DGCLN: multi-head attention-driven dynamic graph convolutional lightweight network for multi-label image classification of kitchen waste

High-Accuracy and Lightweight Image Classification Network for Optimizing Lymphoblastic Leukemia Diagnosisy.

Leukemia is a hematological malignancy that significantly impacts the human immune system. Early detection helps to effectively manage and treat cancer. Although deep learning techniques hold promise for early detection of blood disorders, their effectiveness is often limited by the physical constraints of available datasets and deployed devices. For this investigation, we collect an excellent-quality dataset of 17,826 morphological bone marrow cell images from 85 patients with lymphoproliferative neoplasms. We employ a progressive shrinking approach, which integrates a comprehensive pruning technique across multiple dimensions, including width, depth, resolution, and kernel size, to train our lightweight model. The proposed model achieves rapid identification of acute lymphoblastic leukemia, chronic lymphocytic leukemia, and other bone marrow cell types with an accuracy of 92.51% and a throughput of 111 slides per second, while comprising only 6.4 million parameters. This model significantly contributes to leukemia diagnosis, particularly in the rapid and accurate identification of lymphatic system diseases, and provides potential opportunities to enhance the efficiency and accuracy of medical experts in the diagnosis and treatment of lymphocytic leukemia.

CSA-Net: Channel and Spatial Attention-Based Network for Mammogram and Ultrasound Image Classification.

Breast cancer persists as a critical global health concern, emphasizing the advancement of reliable diagnostic strategies to improve patient survival rates. To address this challenge, a computer-aided diagnostic methodology for breast cancer classification is proposed. An architecture that incorporates a pre-trained EfficientNet-B0 model along with channel and spatial attention mechanisms is employed. The efficiency of leveraging attention mechanisms for breast cancer classification is investigated here. The proposed model demonstrates commendable performance in classification tasks, particularly showing significant improvements upon integrating attention mechanisms. Furthermore, this model demonstrates versatility across various imaging modalities, as demonstrated by its robust performance in classifying breast lesions, not only in mammograms but also in ultrasound images during cross-modality evaluation. It has achieved accuracy of 99.9% for binary classification using the mammogram dataset and 92.3% accuracy on the cross-modality multi-class dataset. The experimental results emphasize the superiority of our proposed method over the current state-of-the-art approaches for breast cancer classification.

HDKD: Hybrid data-efficient knowledge distillation network for medical image classification

Vision Transformers (ViTs) have achieved significant advancement in computer vision tasks due to their powerful modeling capacity. However, their performance notably degrades when trained with insufficient data due to lack of inherent inductive biases. Distilling knowledge and inductive biases from a Convolutional Neural Network (CNN) teacher has emerged as an effective strategy for enhancing the generalization of ViTs on limited datasets. Previous approaches to Knowledge Distillation (KD) have pursued two primary paths: some focused solely on distilling the logit distribution from CNN teacher to ViT student, neglecting the rich semantic information present in intermediate features due to the structural differences between them. Others integrated feature distillation along with logit distillation, yet this introduced alignment operations that limits the amount of knowledge transferred due to mismatched architectures and increased the computational overhead. To this end, this paper presents Hybrid Data-efficient Knowledge Distillation (HDKD) paradigm which employs a CNN teacher and a hybrid student. The choice of hybrid student serves two main aspects. First, it leverages the strengths of both convolutions and transformers while sharing the convolutional structure with the teacher model. Second, this shared structure enables the direct application of feature distillation without any information loss or additional computational overhead. Additionally, we propose an efficient light-weight convolutional block named Mobile Channel-Spatial Attention (MBCSA), which serves as the primary convolutional block in both teacher and student models. Extensive experiments on two medical public datasets showcase the superiority of HDKD over other state-of-the-art models and its computational efficiency. Source code at: https://github.com/omarsherif200/HDKD.