Traditional Deep Convolutional Neural Network Research Articles

An effective colorectal polyp classification for histopathological images based on supervised contrastive learning

Early detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors.

A novel approach for intelligent diagnosis and grading of diabetic retinopathy

Diabetic retinopathy (DR) is a severe ocular complication of diabetes that can lead to vision damage and even blindness. Currently, traditional deep convolutional neural networks (CNNs) used for DR grading tasks face two primary challenges: (1) insensitivity to minority classes due to imbalanced data distribution, and (2) neglecting the relationship between the left and right eyes by utilizing the fundus image of only one eye for training without differentiating between them. To tackle these challenges, we proposed the DRGCNN (DR Grading CNN) model. To solve the problem caused by imbalanced data distribution, our model adopts a more balanced strategy by allocating an equal number of channels to feature maps representing various DR categories. Furthermore, we introduce a CAM-EfficientNetV2-M encoder dedicated to encoding input retinal fundus images for feature vector generation. The number of parameters of our encoder is 52.88 M, which is less than RegNet_y_16gf (80.57 M) and EfficientNetB7 (63.79 M), but the corresponding kappa value is higher. Additionally, in order to take advantage of the binocular relationship, we input fundus retinal images from both eyes of the patient into the network for features fusion during the training phase. We achieved a kappa value of 86.62% on the EyePACS dataset and 86.16% on the Messidor-2 dataset. Experimental results on these representative datasets for diabetic retinopathy (DR) demonstrate the exceptional performance of our DRGCNN model, establishing it as a highly competitive intelligent classification model in the field of DR. The code is available for use at https://github.com/Fat-Hai/DRGCNN.

Art appreciation model design based on improved PageRank and ECA-ResNeXt50 algorithm.

Image sentiment analysis technology can predict, measure and understand the emotional experience of human beings through images. Aiming at the problem of extracting emotional characteristics in art appreciation, this article puts forward an innovative method. Firstly, the PageRank algorithm is enhanced using tweet content similarity and time factors; secondly, the SE-ResNet network design is used to integrate Efficient Channel Attention (ECA) with the residual network structure, and ResNeXt50 is optimized to enhance the extraction of image sentiment features. Finally, the weight coefficients of overall emotions are dynamically adjusted to select a specific emotion incorporation strategy, resulting in effective bimodal fusion. The proposed model demonstrates exceptional performance in predicting sentiment labels, with maximum classification accuracy reaching 88.20%. The accuracy improvement of 21.34% compared to the traditional deep convolutional neural networks (DCNN) model attests to the effectiveness of this study. This research enriches images and texts' emotion feature extraction capabilities and improves the accuracy of emotion fusion classification.

A segmentation model to detect cevical lesions based on machine learning of colposcopic images

BackgroundSemantic segmentation is crucial in medical image diagnosis. Traditional deep convolutional neural networks excel in image classification and object detection but fall short in segmentation tasks. Enhancing the accuracy and efficiency of detecting high-level cervical lesions and invasive cancer poses a primary challenge in segmentation model development. MethodsBetween 2018 and 2022, we retrospectively studied a total of 777 patients, comprising 339 patients with high-level cervical lesions and 313 patients with microinvasive or invasive cervical cancer. Overall, 1554 colposcopic images were put into the DeepLabv3+ model for learning. Accuracy, Precision, Specificity, and mIoU were employed to evaluate the performance of the model in the prediction of cervical high-level lesions and cancer. ResultsExperiments showed that our segmentation model had better diagnosis efficiency than colposcopic experts and other artificial intelligence models, and reached Accuracy of 93.29 %, Precision of 87.2 %, Specificity of 90.1 %, and mIoU of 80.27 %, respectively. ConclutionThe DeepLabv3+ model had good performance in the segmentation of cervical lesions in colposcopic post-acetic-acid images and can better assist colposcopists in improving the diagnosis.

New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural network architecture can be used to directly represent and learn strategies from expert players on different sides. Many game states can be expressed not only as a matrix data structure suitable for DCNN training but also as a graph data structure. Most of the available DCNN methods ignore the territory characteristics of both sides’ positions based on the game rules. Therefore, in this paper, we propose a hybrid approach to the graph neural network to extract the features of the model of game-playing strategies and fuse it into a DCNN. As a graph learning model, graph convolutional networks (GCNs) provide a scheme by which to extract the features in a graph structure, which can better extract the features in the relationship between the game-playing strategies. We validate the work and design a hybrid network to integrate GCNs and DCNNs in the game of Go and show that on the KGS Go dataset, the performance of the hybrid model outperforms the traditional DCNN model. The hybrid model demonstrates a good performance in extracting the game strategy of Go.

An Efficient, Lightweight, Tiny 2D-CNN Ensemble Model to Detect Cardiomegaly in Heart CT Images.

Cardiomegaly is a significant global health concern, especially in developing nations. Although advanced clinical care is available for newly diagnosed patients, many in resource-limited regions face late diagnoses and consequent increased mortality. This challenge is accentuated by a scarcity of radiography equipment and radiologists. Hence, we propose the development of a computer-aided diagnostic (CAD) system, specifically a lightweight, tiny 2D-CNN ensemble model, to facilitate early detection and, potentially, reduce mortality rates. Deep learning, with its subset of convolutional neural networks (CNN), has shown potential in visual applications, especially in medical image diagnosis. However, traditional deep CNNs often face compatibility issues with object-oriented human factor technology. Our proposed model aims to bridge this gap. Using CT scan images sourced from the Mendeley data center, our tiny 2D-CNN ensemble learning model achieved an accuracy of 96.32%, offering a promising tool for efficient and accurate cardiomegaly detection.

On the Imaginary Wings: Text-Assisted Complex-Valued Fusion Network for Fine-Grained Visual Classification.

Fine-grained visual classification (FGVC) is challenging due to the interclass similarity and intraclass variation in datasets. In this work, we explore the great merit of complex values in introducing an imaginary part for modeling data uncertainty (e.g., different points on the complex plane can describe the same state) and graph convolutional networks (GCNs) in learning interdependently among classes to simultaneously tackle the above two major challenges. To the end, we propose a novel approach, termed text-assisted complex-valued fusion network (TA-CFN). Specifically, we expand each feature from 1-D real values to 2-D complex value by disassembling feature maps, thereby enabling the extension of traditional deep convolutional neural networks over the complex domain. Then, we fuse the real and imaginary parts of complex features through complex projection and modulus operation. Finally, we build an undirected graph over the object labels with the assistance of a text corpus, and a GCN is learned to map this graph into a set of classifiers. The benefits are in two folds: 1) complex features allow for a richer algebraic structure to better model the large variation within the same category and 2) leveraging the interclass dependencies brought by the GCN to capture key factors of the slight variation among different categories. We conduct extensive experiments to verify that our proposed model can achieve the state-of-the-art performance on two widely used FGVC datasets.

Optimal Design of Antennas based on CNN and BLS

Surrogate can greatly reduce the computing cost of full-wave electromagnetic simulation software in antenna design, and neural networks as a kind of surrogate have become very popular in recent years, among which the Convolutional Neural Network (CNN) is a very hot topic due to its better feature extraction and generalization capability. In this paper, Broad Learning System (BLS) is introduced to address the problems that CNN may fall into with local optimization and overfitting when dealing with antenna data. It provides a learning method to increase nodes in width for regression prediction, which has good generalization ability. Specifically, in antenna design, feature extraction is first applied using the convolution and pooling of CNN. The original dataset is combined with the extracted feature data to realize data augmentation, and then the BLS is used for regression training. Compared with the traditional deep CNN, this model uses lightweight convolutional layers, which is promising in solving the problem of the network falling into local optimum or overfitting. The simulation results show that the accuracy reaches 93.91% and 92.34% respectively on the Abalone benchmark and antenna data, proving the effectiveness of the proposed model in this paper.

Triplet-Net Classification of Contiguous Stem Cell Microscopy Images.

Cellular microscopy imaging is a common form of data acquisition for biological experimentation. Observation of gray-level morphological features allows for the inference of useful biological information such as cellular health and growth status. Cellular colonies can contain multiple cell types, making colony level classification very difficult. Additionally, cell types growing in a hierarchical, downstream fashion, can often look visually similar, although biologically distinct. In this paper, it is determined empirically that traditional deep Convolutional Neural Networks (CNN) and classical object recognition techniques are not sufficient to distinguish between these subtle visual differences, resulting in misclassifications. Instead, Triplet-net CNN learning is employed in a hierarchical classification scheme to improve the ability of the model to discern distinct, fine-grain features of two commonly confused morphological image-patch classes, namely Dense and Spread colonies. The Triplet-net method improves classification accuracy over a four-class deep neural network by ∼ 3 %, a value that was determined to be statistically significant, as well as existing state-of-the-art image patch classification approaches and standard template matching. These findings allow for the accurate classification of multi-class cell colonies with contiguous boundaries, and increased reliability and efficiency of automated, high-throughput experimental quantification using non-invasive microscopy.

BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications

Certain life-threatening abnormalities, such as cholangiocarcinoma, in the human biliary tract are curable if detected at an early stage, and ultrasonography has been proven to be an effective tool for identifying them. However, the diagnosis often requires a second opinion from experienced radiologists, who are usually overwhelmed by many cases. Therefore, we propose a deep convolutional neural network model, named biliary tract network (BiTNet), developed to solve problems in the current screening system and to avoid overconfidence issues of traditional deep convolutional neural networks. Additionally, we present an ultrasound image dataset for the human biliary tract and demonstrate two artificial intelligence (AI) applications: auto-prescreening and assisting tools. The proposed model is the first AI model to automatically screen and diagnose upper-abdominal abnormalities from ultrasound images in real-world healthcare scenarios. Our experiments suggest that prediction probability has an impact on both applications, and our modifications to EfficientNet solve the overconfidence problem, thereby improving the performance of both applications and of healthcare professionals. The proposed BiTNet can reduce the workload of radiologists by 35% while keeping the false negatives to as low as 1 out of every 455 images. Our experiments involving 11 healthcare professionals with four different levels of experience reveal that BiTNet improves the diagnostic performance of participants of all levels. The mean accuracy and precision of the participants with BiTNet as an assisting tool (0.74 and 0.61, respectively) are statistically higher than those of participants without the assisting tool (0.50 and 0.46, respectively (p<0.001)). These experimental results demonstrate the high potential of BiTNet for use in clinical settings.

A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning

Rapid and accurate identification of moving load is crucial for bridge operation management and early warning of overload events. However, it is hard to obtain them rapidly via traditional machine learning methods, due to their massive model parameters and complex network structure. To this end, this paper proposes a novel method to perform moving loads identification using MobileNetV2 and transfer learning. Specifically, the dynamic responses of a vehicle–bridge interaction system are firstly transformed into a two-dimensional time-frequency image by continuous wavelet transform to construct the database. Secondly, a pre-trained MobileNetV2 model based on ImageNet is transferred to the moving load identification task by transfer learning strategy for describing the mapping relationship between structural response and these specified moving loads. Then, load identification can be performed through inputting bridge responses into the established relationship. Finally, the effectiveness of the method is verified by numerical simulation. The results show that it can accurately identify the vehicle weight, vehicle speed information, and presents excellent strong robustness. In addition, MobileNetV2 has faster identification speed and requires less computational resources than several traditional deep convolutional neural network models in moving load identification, which can provide a novel idea for the rapid identification of moving loads.

HC-Net: A hybrid convolutional network for non-human primate brain extraction.

Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks.

A Modeling Design Method for Complex Products Based on LSTM Neural Network and Kansei Engineering

Complex products (CPs) modeling design has a long development cycle and high cost, and it is difficult to accurately meet the needs of enterprises and users. At present, the Kansei Engineering (KE) method based on back-propagated (BP) neural networks is applied to solve the modeling design problem that meets users’ affective preferences for simple products quickly and effectively. However, the modeling feature data of CPs have a wide range of dimensions, long parameter codes, and the characteristics of time series. As a result, it is difficult for BP neural networks to recognize the affective preferences of CPs from an overall visual perception level as humans do. To address the problems above and assist designers with efficient and high-quality design, a CP modeling design method based on Long Short-Term Memory (LSTM) neural network and KE (CP-KEDL) was proposed. Firstly, the improved MA method was carried out to transform the product modeling features into feature codes with sequence characteristics. Secondly, the mapping model between perceptual images and modeling features was established based on the LSTM neural network to predict the evaluation value of the product’s perceptual images. Finally, the optimal feature sets were calculated by a Genetic Algorithm (GA). The experimental results show that the MSE of the LSTM model is only 0.02, whereas the MSE of the traditional Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) neural network models are 0.30 and 0.23, respectively. The results verified that the proposed method can effectively grapple with the CP modeling design problem with the timing factor, improve design satisfaction and shorten the R&amp;D cycle of CP industrial design.

Adaptive Threshold Hierarchical Incremental Learning Method

Traditional deep convolutional neural networks have achieved excellent performance on various machine learning tasks. Still, they perform poorly in continuous data stream environments, where models trained on new datasets often suffer from a significant drop in performance on old datasets, a phenomenon known as “catastrophic forgetting.” Incremental learning can help solve the “catastrophic forgetting” problem in deep learning by learning new knowledge while retaining what has already been learned. In practice, incremental learning algorithms usually need to be deployed on edge devices with limited memory and restricted access to training data, facing the problems of high model complexity and imbalance between old and new categories of data. We propose an Adaptive Threshold Hierarchical Incremental Learning (ATHIL) method to address the above problems. Our proposed method does not require additional data and model storage space during the training process, combines local weight discrete coefficient thresholding and the mean nearest neighbor principle, uses a sparse matrix hierarchical masking network, and flexibly adjusts the network structure according to different tasks to achieve learning multiple image classification tasks in a single network. The experimental results show that the performance of the proposed method significantly outperforms existing methods on fine-grained classification datasets under three evaluation metrics.

Improved Wafer Map Inspection Using Attention Mechanism and Cosine Normalization

Wafer map inspection is essential for semiconductor manufacturing quality control and analysis. The deep convolutional neural network (DCNN) is the most effective algorithm in wafer defect pattern analysis. Traditional DCNNs rely heavily on high quality datasets for training. However, obtaining balanced and sufficient labeled data is difficult in practice. This paper reconsiders the causes of the imbalance and proposes a deep learning method that can learn robust knowledge from an imbalanced dataset using the attention mechanism and cosine normalization. We interpret the dataset imbalance as both a feature and a quantity distribution imbalance. To compensate for feature distribution imbalance, we add an improved convolutional attention module to the DCNN to enhance representation. In particular, a feature-map-specific direction mapping module is developed to amplify the positional information of defect clusters. For quantity distribution imbalance, the cosine normalization algorithm is proposed to replace the fully connected layer, and classifier fine-tuning is realized through a small amount of iterative training, which decreases the sensitivity to the quantitative distribution. The experimental results on real-world datasets demonstrate that the proposed method significantly improves the robustness of wafer map inspection and outperforms existing algorithms when trained on imbalanced datasets.

A Modified Residual Network Based on Multi-scale Segmentation for Aerobics Motion Image Recognition

&lt;p&gt;Image recognition is an important field in artificial intelligence, it makes use of the computer to conduct image processing, analysis and understanding to recognize a variety of different objects. And it uses a series of enhancement and reconstruction methods to effectively improve the image quality. Traditional deep Convolutional Neural Network (DCNN) not only improves the recognition accuracy, but also reduces the recognition speed. How to improve the speed while maintaining the accuracy has become an important direction in image recognition. In this paper, we propose a modified Residual network based on multi-scale segmentation for aerobics motion image recognition. The new residual network has the characteristics of shorter network length and faster recognition speed. First, it reduces the length of the network and gets a new residual network with seven layers. Then, combining with the multi-scale segmentation method, an image recognition residual network is obtained. Finally, experiments on the CIFAR10 dataset, the results show that the proposed new motion image recognition method has better recognition accuracy and faster recognition speed.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Low-SNR Recognition of UAV-to-Ground Targets Based on Micro-Doppler Signatures Using Deep Convolutional Denoising Encoders and Deep Residual Learning

The rapid development of flight control technology has made unmanned aerial vehicles (UAVs) widely used in high-precision strikes on the battlefield. The premise of this is to achieve accurate target recognition using UAV-based radars. Aiming at three typical ground targets, including pedestrians, wheeled vehicles, and tracked vehicles, the micro-Doppler modulation caused by the random vibration of the UAV is analyzed in this article for the first time. To improve the recognition accuracy under low signal-to-noise ratios (SNRs), Doppler signals are transformed into time–frequency images, and a deep convolutional denoising encoder (DCDE) is designed to effectively remove the noise without suppressing micro-Doppler characteristics. To avoid the complicated micro-Doppler feature extraction, deep residual learning that can reduce the burden of network training and gain higher learning efficiency compared with traditional deep convolutional neural networks (DCNNs) is adopted. Recognition results under various occasions using denoised micro-Doppler images and designed residual learning network indicate that the proposed method has higher precision and better robustness than current methods. Even when the SNR is only −16 dB, the overall recognition accuracy still exceeds 90%.

Teacher-Student Based Domain Adaptation for Person Re-identification

In traditional deep Convolutional Neural Network (CNN) based person re-identification (Re-ID) methods, there must be thousands of training samples with annotated information under the same data distribution, in other words, the data used to train the CNN model is collected from the same scene. However, if we need to deploy a trained Re-ID model at the airport, but the data for training the model is collected on campus, the traditional methods will suffer considerable performance degradation because the data come from different distributions. It is a non-trivial task to straightforwardly re-collect sufficient annotations from the new scene, due to the high cost of time and human labor. Therefore, the domain adaptation paradigm is used to resolve the data distribution problem. Specifically, an efficacious method for domain adaptive person Re-ID based on the teacher-student framework is proposed in our research. Our method has been evaluated by numerous experiments and proved its effectiveness.

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

High-Resolution Boundary Refined Convolutional Neural Network for Automatic Agricultural Greenhouses Extraction from GaoFen-2 Satellite Imageries

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs.