Pub Date : 2023-07-08DOI: 10.1142/s0219467824500529
P. Mangai, M. Geetha, G. Kumaravelan
Identifying events using surveillance videos is a major source that reduces crimes and illegal activities. Specifically, abnormal event detection gains more attention so that immediate responses can be provided. Video processing using conventional techniques identifies the events but fails to categorize them. Recently deep learning-based video processing applications provide excellent performances however the architecture considers either spatial or temporal features for event detection. To enhance the detection rate and classification accuracy in abnormal event detection from video keyframes, it is essential to consider both spatial and temporal features. Earlier approaches consider any one of the features from keyframes to detect the anomalies from video frames. However, the results are not accurate and prone to errors sometimes due to video environmental and other factors. Thus, two-stream hybrid deep learning architecture is presented to handle spatial and temporal features in the video anomaly detection process to attain enhanced detection performances. The proposed hybrid models extract spatial features using YOLO-V4 with VGG-16, and temporal features using optical FlowNet with VGG-16. The extracted features are fused and classified using hybrid CNN-LSTM model. Experimentation using benchmark UCF crime dataset validates the proposed model performances over existing anomaly detection methods. The proposed model attains maximum accuracy of 95.6% which indicates better performance compared to state-of-the-art techniques.
{"title":"Two-Stream Spatial–Temporal Feature Extraction and Classification Model for Anomaly Event Detection Using Hybrid Deep Learning Architectures","authors":"P. Mangai, M. Geetha, G. Kumaravelan","doi":"10.1142/s0219467824500529","DOIUrl":"https://doi.org/10.1142/s0219467824500529","url":null,"abstract":"Identifying events using surveillance videos is a major source that reduces crimes and illegal activities. Specifically, abnormal event detection gains more attention so that immediate responses can be provided. Video processing using conventional techniques identifies the events but fails to categorize them. Recently deep learning-based video processing applications provide excellent performances however the architecture considers either spatial or temporal features for event detection. To enhance the detection rate and classification accuracy in abnormal event detection from video keyframes, it is essential to consider both spatial and temporal features. Earlier approaches consider any one of the features from keyframes to detect the anomalies from video frames. However, the results are not accurate and prone to errors sometimes due to video environmental and other factors. Thus, two-stream hybrid deep learning architecture is presented to handle spatial and temporal features in the video anomaly detection process to attain enhanced detection performances. The proposed hybrid models extract spatial features using YOLO-V4 with VGG-16, and temporal features using optical FlowNet with VGG-16. The extracted features are fused and classified using hybrid CNN-LSTM model. Experimentation using benchmark UCF crime dataset validates the proposed model performances over existing anomaly detection methods. The proposed model attains maximum accuracy of 95.6% which indicates better performance compared to state-of-the-art techniques.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42437675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-07DOI: 10.1142/s0219467824500499
Yanxi Wei
With the development of computer technology, image stylization has become one of the hottest technologies in image processing. To optimize the effect of artistic image style conversion, a method of artistic image style conversion optimized by attention mechanism is proposed. The CycleGAN network model is introduced, and then the generator is optimized by the attention mechanism. Finally, the application effect of the improved model is tested and analyzed. The results show that the improved model tends to be stable after 40 iterations, the loss value remains at 0.3, and the PSNR value can reach up to 15. From the perspective of the generated image effect, the model has a better visual effect than the CycleGAN model. In the subjective evaluation, 63 people expressed satisfaction with the converted artistic image. As a result, the cyclic generative adversarial network model optimized by the attention mechanism improves the clarity of the generated image, enhances the effect of blurring the target boundary contour, retains the detailed information of the image, optimizes the image stylization effect, and improves the image quality of the method and application value of the processing field.
{"title":"Artistic Image Style Transfer Based on CycleGAN Network Model","authors":"Yanxi Wei","doi":"10.1142/s0219467824500499","DOIUrl":"https://doi.org/10.1142/s0219467824500499","url":null,"abstract":"With the development of computer technology, image stylization has become one of the hottest technologies in image processing. To optimize the effect of artistic image style conversion, a method of artistic image style conversion optimized by attention mechanism is proposed. The CycleGAN network model is introduced, and then the generator is optimized by the attention mechanism. Finally, the application effect of the improved model is tested and analyzed. The results show that the improved model tends to be stable after 40 iterations, the loss value remains at 0.3, and the PSNR value can reach up to 15. From the perspective of the generated image effect, the model has a better visual effect than the CycleGAN model. In the subjective evaluation, 63 people expressed satisfaction with the converted artistic image. As a result, the cyclic generative adversarial network model optimized by the attention mechanism improves the clarity of the generated image, enhances the effect of blurring the target boundary contour, retains the detailed information of the image, optimizes the image stylization effect, and improves the image quality of the method and application value of the processing field.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48180670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-07DOI: 10.1142/s0219467825500068
Sita M. Yadav, S. Chaware
Video content analysis (VCA) is the process of analyzing the contents in the video for various applications. Video classification and content analysis are two of the most difficult challenges that computer vision researchers must solve. Object detection plays an important role in the VCA and is used for identification, detection and classification of objects in the images. The Chaser Prairie Wolf optimization-based deep Convolutional Neural Network classifier (CPW opt-deep CNN classifier) is used in this research to identify and classify the objects in the videos. The deep CNN classifier correctly detected the objects in the video, and the CPW optimization boosted the deep CNN classifier’s performance, where the decision-making behavior of the chasers is enhanced by the sharing nature of the prairie wolves. The classifier’s parameters were successfully tuned by the enabled optimization, which also aids in producing better results. The Ensemble model developed for the object detection adds value to the research and is initiated by the standard hybridization of the YOLOv4 and Resnet 101 model, which evaluated the research’s accuracy, sensitivity, and specificity, improving its efficacy. The proposed CPW opt-deep CNN classifier attained the values of 89.74%, 89.50%, and 89.19% while classifying objects in dataset 1, 91.66%, 86.01%, and 91.52% while classifying objects in dataset 2, compared to the preceding method that is efficient.
视频内容分析（VCA）是为各种应用分析视频中的内容的过程。视频分类和内容分析是计算机视觉研究人员必须解决的两个最困难的挑战。目标检测在VCA中起着重要作用，用于识别、检测和分类图像中的目标。本研究使用基于Chaser Prairie Wolf优化的深度卷积神经网络分类器（CPW opt deep CNN分类器）对视频中的对象进行识别和分类。深度CNN分类器正确地检测到了视频中的对象，CPW优化提高了深度CNN分类器的性能，草原狼的共享性增强了追逐者的决策行为。启用的优化成功地调整了分类器的参数，这也有助于产生更好的结果。为物体检测开发的Ensemble模型为研究增加了价值，由YOLOv4和Resnet 101模型的标准杂交启动，该模型评估了研究的准确性、敏感性和特异性，提高了其疗效。与前面的有效方法相比，所提出的CPW opt deep CNN分类器在对数据集1中的对象进行分类时获得了89.74%、89.50%和89.19%的值，在对数据集中2的对象进行归类时获得了91.66%、86.01%和91.52%的值。
{"title":"Detection and Classification of Objects in Video Content Analysis Using Ensemble Convolutional Neural Network Model","authors":"Sita M. Yadav, S. Chaware","doi":"10.1142/s0219467825500068","DOIUrl":"https://doi.org/10.1142/s0219467825500068","url":null,"abstract":"Video content analysis (VCA) is the process of analyzing the contents in the video for various applications. Video classification and content analysis are two of the most difficult challenges that computer vision researchers must solve. Object detection plays an important role in the VCA and is used for identification, detection and classification of objects in the images. The Chaser Prairie Wolf optimization-based deep Convolutional Neural Network classifier (CPW opt-deep CNN classifier) is used in this research to identify and classify the objects in the videos. The deep CNN classifier correctly detected the objects in the video, and the CPW optimization boosted the deep CNN classifier’s performance, where the decision-making behavior of the chasers is enhanced by the sharing nature of the prairie wolves. The classifier’s parameters were successfully tuned by the enabled optimization, which also aids in producing better results. The Ensemble model developed for the object detection adds value to the research and is initiated by the standard hybridization of the YOLOv4 and Resnet 101 model, which evaluated the research’s accuracy, sensitivity, and specificity, improving its efficacy. The proposed CPW opt-deep CNN classifier attained the values of 89.74%, 89.50%, and 89.19% while classifying objects in dataset 1, 91.66%, 86.01%, and 91.52% while classifying objects in dataset 2, compared to the preceding method that is efficient.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48347690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-07DOI: 10.1142/s0219467824500578
G. Ashwini, T. Ramashri, Mohammad Rasheed Ahmed
The prominence and popularity of Image Denoising in medical image processing has been obvious since its early conception. Medical Image Denoising is primarily a significant pre-processing method for further image processing steps in various fields. Its ability to speed up the diagnosis by enhancing the sensory quality of noisy images is proven to be working in most of the cases. The efficiency of the deep neural networks for Medical Image Denoising has been well proven traditionally. Both noisy and clean images are equal requirements in most of these training methods. However, it is not always possible to procure clean images for various applications such as Dynamic Imaging, Computed Tomography, Magnetic Resonance Imaging, and Camera Photography due to the inevitable presence of naturally occurring noisy signals which are intrinsic to the images. There have been self-supervised single Image Denoising methods proposed recently. Being inspired by these methods, taking this a step further, we propose a novel and better denoising method for single images by training the learning model on each of the channels of the input data, which is termed as “Noise2Split”. It ultimately proves to reduce the noise granularly in each channel, pixel by pixel, by using Single Channeled Patch-Based (SCPB) learning, which is found to be resulting in a better performance. Further, to obtain optimum results, the method leverages BRISQUE image quality assessment. The model is demonstrated on X-ray, CT, PET, Microscopy, and real-world noisy images.
{"title":"Noise2Split — Single Image Denoising Via Single Channeled Patch-Based Learning","authors":"G. Ashwini, T. Ramashri, Mohammad Rasheed Ahmed","doi":"10.1142/s0219467824500578","DOIUrl":"https://doi.org/10.1142/s0219467824500578","url":null,"abstract":"The prominence and popularity of Image Denoising in medical image processing has been obvious since its early conception. Medical Image Denoising is primarily a significant pre-processing method for further image processing steps in various fields. Its ability to speed up the diagnosis by enhancing the sensory quality of noisy images is proven to be working in most of the cases. The efficiency of the deep neural networks for Medical Image Denoising has been well proven traditionally. Both noisy and clean images are equal requirements in most of these training methods. However, it is not always possible to procure clean images for various applications such as Dynamic Imaging, Computed Tomography, Magnetic Resonance Imaging, and Camera Photography due to the inevitable presence of naturally occurring noisy signals which are intrinsic to the images. There have been self-supervised single Image Denoising methods proposed recently. Being inspired by these methods, taking this a step further, we propose a novel and better denoising method for single images by training the learning model on each of the channels of the input data, which is termed as “Noise2Split”. It ultimately proves to reduce the noise granularly in each channel, pixel by pixel, by using Single Channeled Patch-Based (SCPB) learning, which is found to be resulting in a better performance. Further, to obtain optimum results, the method leverages BRISQUE image quality assessment. The model is demonstrated on X-ray, CT, PET, Microscopy, and real-world noisy images.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46915734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.1142/s0219467824500414
R. V. Prasad, J. Prasad, B. Chaudhari, Nihar M. Ranjan, Rajat Srivastava
Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively.
洪水是致命的灾难性灾害，会造成生命损失和财产、农田和基础设施的破坏。为了解决这个问题，有必要设计和采用一个有效的洪水管理系统，可以立即识别洪水区域，并尽快采取救援措施。因此，本研究开发了一种有效的洪水检测方法——基于反电晕洗牌牧羊人优化算法的深度量子神经网络(ACSSOA-based Deep Quantum Neural Network，简称Deep QNN)来识别洪水泛滥区域。在这里，使用空间约束多核距离模糊c均值(MKFCM_S)进行分割过程，其中模糊c均值(FCM)使用基于核诱导距离的空间约束(KFCM_S)进行修改。对于洪水检测，已经使用了深度QNN，其中深度QNN的训练过程是使用设计的优化算法ACSSOA完成的。此外，所设计的ACSSOA是由抗冠状病毒优化算法(ACVO)和shuffle Shepherd优化算法(SSOA)杂交而成的。利用喀拉拉邦洪水数据库对该方法进行了评价，结果表明，该方法的分割精度、检测精度、灵敏度和特异性最高，分别为0.904、0.914、0.927和0.920。
{"title":"FCM with Spatial Constraint Multi-Kernel Distance-Based Segmentation and Optimized Deep Learning for Flood Detection","authors":"R. V. Prasad, J. Prasad, B. Chaudhari, Nihar M. Ranjan, Rajat Srivastava","doi":"10.1142/s0219467824500414","DOIUrl":"https://doi.org/10.1142/s0219467824500414","url":null,"abstract":"Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45182967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-07DOI: 10.1142/s0219467824500451
M. Prashanthi, M. Chandra Mohan
The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64%, maximum sensitivity of 95.14%, maximum specificity of 99%, maximum [Formula: see text]-score of 93.53%, and maximum precision of 99% by considering the [Formula: see text]-fold.
{"title":"Hybrid Optimization-Based Neural Network Classifier for Software Defect Prediction","authors":"M. Prashanthi, M. Chandra Mohan","doi":"10.1142/s0219467824500451","DOIUrl":"https://doi.org/10.1142/s0219467824500451","url":null,"abstract":"The software is applied in various areas so the quality of the software is very important. The software defect prediction (SDP) is used to solve the software issues and enhance the quality. The robustness and reliability are the major concerns in the existing SDP approaches. Hence, in this paper, the hybrid optimization-based neural network (Optimized NN) is developed for the effective detection of the defects in the software. The two main steps involved in the Optimized NN-based SDP are feature selection and SDP utilizing Optimized NN. The data is fed forwarded to the feature selection module, where relief algorithm selects the significant features relating to the defect and no-defects. The features are fed to the SDP module, and the optimal tuning of NN classifier is obtained by the hybrid optimization developed by the integration of the social spider algorithm (SSA) and gray wolf optimizer (GWO). The comparative analysis of the developed prediction model reveals the effectiveness of the proposed method that attained the maximum accuracy of 93.64%, maximum sensitivity of 95.14%, maximum specificity of 99%, maximum [Formula: see text]-score of 93.53%, and maximum precision of 99% by considering the [Formula: see text]-fold.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49066729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.1142/s0219467824500384
K. P. Kumar, M. Rao, M. Venkatanarayana
Nowadays, the image degradation field suffers from several challenges while processing underwater color images including color distortion and image blurring due to the scattering media. Moreover, to get appropriate multi-frame super-resolution images, there is essential for recovering a better quantity of images. Traditionally, the shift among images is directly evaluated when considering the under-sampled Low-Resolution (LR) images. On the other hand, the high-frequency LR image faces unreliability owing to the aliasing consequences of sub-sampling, but it will also degrade the recovery accuracy. This task design implements a novel image recovery model from the moving water surface by adopting the multi-objective adaptive higher-order spectral analysis. Image pre-processing, lucky region selection, and image recovery are the three main phases of this model. The bicoherence method and dice coefficient method are adopted for performing the lucky region selection. Finally, the adoption of the multi-objective adaptive bispectra method is used for performing the image recovery from the moving water surface. The improved Adaptive Fitness-oriented Random number-based Galactic Swarm Optimization (AFR-GSO) algorithm is used for optimizing the constraints of the bispectrum method. The experimental results verify the enrichment of image quality by the proposed model over the existing techniques.
{"title":"A Novel Image Recovery from Moving Water Surface Using Multi-Objective Bispectrum Method","authors":"K. P. Kumar, M. Rao, M. Venkatanarayana","doi":"10.1142/s0219467824500384","DOIUrl":"https://doi.org/10.1142/s0219467824500384","url":null,"abstract":"Nowadays, the image degradation field suffers from several challenges while processing underwater color images including color distortion and image blurring due to the scattering media. Moreover, to get appropriate multi-frame super-resolution images, there is essential for recovering a better quantity of images. Traditionally, the shift among images is directly evaluated when considering the under-sampled Low-Resolution (LR) images. On the other hand, the high-frequency LR image faces unreliability owing to the aliasing consequences of sub-sampling, but it will also degrade the recovery accuracy. This task design implements a novel image recovery model from the moving water surface by adopting the multi-objective adaptive higher-order spectral analysis. Image pre-processing, lucky region selection, and image recovery are the three main phases of this model. The bicoherence method and dice coefficient method are adopted for performing the lucky region selection. Finally, the adoption of the multi-objective adaptive bispectra method is used for performing the image recovery from the moving water surface. The improved Adaptive Fitness-oriented Random number-based Galactic Swarm Optimization (AFR-GSO) algorithm is used for optimizing the constraints of the bispectrum method. The experimental results verify the enrichment of image quality by the proposed model over the existing techniques.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45247148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-22DOI: 10.1142/s0219467824500360
Fuxiang Liu, Chen Zang, Junqi Shi, Weiyu He, Yubo Liang, Lei Li
Aiming at the new coronavirus that appeared in 2019, which has caused a large number of infected patients worldwide due to its high contagiousness, in order to detect the source of infection in time and cut off the chain of transmission, we developed a new Chest X-ray (CXR) image classification algorithm with high accuracy, simple operation and fast processing for COVID-19. The algorithm is based on ConvNeXt pure convolutional neural network, we adjusted the network structure and loss function, added some new Data Augmentation methods and introduced attention mechanism. Compared with other classical convolutional neural network classification algorithms such as AlexNet, ResNet-34, ResNet-50, ResNet-101, ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the improved algorithm has better performance on COVID dataset.
{"title":"An Improved COVID-19 Lung X-Ray Image Classification Algorithm Based on ConvNeXt Network","authors":"Fuxiang Liu, Chen Zang, Junqi Shi, Weiyu He, Yubo Liang, Lei Li","doi":"10.1142/s0219467824500360","DOIUrl":"https://doi.org/10.1142/s0219467824500360","url":null,"abstract":"Aiming at the new coronavirus that appeared in 2019, which has caused a large number of infected patients worldwide due to its high contagiousness, in order to detect the source of infection in time and cut off the chain of transmission, we developed a new Chest X-ray (CXR) image classification algorithm with high accuracy, simple operation and fast processing for COVID-19. The algorithm is based on ConvNeXt pure convolutional neural network, we adjusted the network structure and loss function, added some new Data Augmentation methods and introduced attention mechanism. Compared with other classical convolutional neural network classification algorithms such as AlexNet, ResNet-34, ResNet-50, ResNet-101, ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the improved algorithm has better performance on COVID dataset.","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43948823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-05DOI: 10.1142/s021946782450044x
K. Visalini, Saravanan Alagarsamy, S. Raja
Studies deem that about 1 percent of the human population is affected by epileptic seizures on a global scale. It is characterized as an undue neuronal discharge in the brain and degrades the quality of life of the patients to a large extent. Children being unaware of a sudden onset of seizures could be affected by severe injury or even mortality. Machine-learning-based epileptic seizure detection from EEG (Electro-Encephalogram) signals have always been a hot area of research. However, the majority of the research works rely on correlated non-linear features extracted from the EEG signals, causing a high-computational overhead, and challenging their application in real-time clinical diagnosis. This study proposes a robust seizure detection framework using Gaussian Deep Boltzmann Machine-based classifier and Symplectic Geometric Decomposition (SGD)-based features. The simplified eigenvalues derived through Symplectic Similarity Transform (SST) are employed as feature vectors for the classifier, eliminating the need for a deliberate feature extraction procedure. The study examines the transferability capability of the suggested framework in discriminating seizures in both neonates and pediatric subjects in unison, experimenting with classical annotated datasets. The model yielded a mean accuracy of about 97.91% and an F1 Score of 0.935 in pediatric seizure detection, and mean sensitivity and specificity of 99.05% and 98.28%, in neonatal seizure detection tasks, respectively. Thus, the model can be deemed comparable to the available state-of-the-art seizure detection frameworks.
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Pub Date : 2023-04-12DOI: 10.1142/s0219467823400090
Y. Sravani Devi, S. Phani Kumar
{"title":"A Deep Convolutional Generative Adversarial Network (DC-GAN) and Variational Auto Encoders (VAE) Models with Transfer Learning Approaches for Diabetic Retinopathy Detection","authors":"Y. Sravani Devi, S. Phani Kumar","doi":"10.1142/s0219467823400090","DOIUrl":"https://doi.org/10.1142/s0219467823400090","url":null,"abstract":"","PeriodicalId":44688,"journal":{"name":"International Journal of Image and Graphics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45124464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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