Yang Zhang¹, Liru Qiu², Yongkai Zhu¹, Long Wen^1,*, Xiaoping Luo^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 873-894, 2022, DOI:10.32604/cmes.2022.022322

Abstract Pneumonia is part of the main diseases causing the death of children. It is generally diagnosed through chest X-ray images. With the development of Deep Learning (DL), the diagnosis of pneumonia based on DL has received extensive attention. However, due to the small difference between pneumonia and normal images, the performance of DL methods could be improved. This research proposes a new fine-grained Convolutional Neural Network (CNN) for children’s pneumonia diagnosis (FG-CPD). Firstly, the fine-grained CNN classification which can handle the slight difference in images is investigated. To obtain the raw images from the real-world chest X-ray data, the YOLOv4… More >

Ng Lai Yee¹, Mohd Asyraf Zulkifley^2,*, Adhi Harmoko Saputro³, Siti Raihanah Abdani⁴

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5331-5348, 2022, DOI:10.32604/cmc.2022.028801

Abstract Micro-expression is manifested through subtle and brief facial movements that relay the genuine person’s hidden emotion. In a sequence of videos, there is a frame that captures the maximum facial differences, which is called the apex frame. Therefore, apex frame spotting is a crucial sub-module in a micro-expression recognition system. However, this spotting task is very challenging due to the characteristics of micro-expression that occurs in a short duration with low-intensity muscle movements. Moreover, most of the existing automated works face difficulties in differentiating micro-expressions from other facial movements. Therefore, this paper presents a deep learning model with an attention… More >

Shun Wang¹, Lin Qiao², Wei Fang³, Guodong Jing⁴, Victor S. Sheng⁵, Yong Zhang^1,*

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 673-687, 2022, DOI:10.32604/cmc.2022.028411

Abstract PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the temporal dependence of the long-term… More >

Can Xiao^1,2, Feng Li^1,2,*, Dengyong Zhang^1,2, Pu Huang^1,2, Xiangling Ding³, Victor S. Sheng⁴

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1145-1154, 2022, DOI:10.32604/csse.2022.027249

Abstract Image inpainting based on deep learning has been greatly improved. The original purpose of image inpainting was to repair some broken photos, such as inpainting artifacts. However, it may also be used for malicious operations, such as destroying evidence. Therefore, detection and localization of image inpainting operations are essential. Recent research shows that high-pass filtering full convolutional network (HPFCN) is applied to image inpainting detection and achieves good results. However, those methods did not consider the spatial location and channel information of the feature map. To solve these shortcomings, we introduce the squeezed excitation blocks (SE) and propose a high-pass… More >

Xin Zihao¹, Wang Hongyuan^1,*, Qi Pengyu¹, Du Weidong², Zhang Ji¹, Chen Fuhua³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5925-5938, 2022, DOI:10.32604/cmc.2022.026943

Abstract For a long time, the detection and extraction of printed surface defects has been a hot issue in the print industry. Nowadays, defect detection of a large number of products still relies on traditional image processing algorithms such as scale invariant feature transform (SIFT) and oriented fast and rotated brief (ORB), and researchers need to design algorithms for specific products. At present, a large number of defect detection algorithms based on object detection have been applied but need lots of labeling samples with defects. Besides, there are many kinds of defects in printed surface, so it is difficult to enumerate… More >

Weiwei Cai^1,2, Yaping Song¹, Huan Duan¹, Zhenwei Xia¹, Zhanguo Wei^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1539-1555, 2022, DOI:10.32604/cmes.2022.019785

Abstract In the smart logistics industry, unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans. Therefore, they play a critical role in smart warehousing, and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets. However, most current recognition algorithms are ineffective due to the diverse types of pallets, their complex shapes, frequent blockades in production environments, and changing lighting conditions. This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention (MFMBA) neural network for logistics pallet segmentation. To better predict… More >

Yintong Wang^1,2,*, Yingjie Yang², Haiyan Chen³, Hao Zheng¹, Heyou Chang¹

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 371-388, 2022, DOI:10.32604/iasc.2022.027146

Abstract Handwritten Chinese recognition which involves variant writing style, thousands of character categories and monotonous data mark process is a long-term focus in the field of pattern recognition research. The existing methods are facing huge challenges including the complex structure of character/line-touching, the discriminate ability of similar characters and the labeling of training datasets. To deal with these challenges, an end-to-end residual attention handwritten Chinese paragraph text recognition method is proposed, which uses fully convolutional neural networks as the main structure of feature extraction and employs connectionist temporal classification as a loss function. The novel residual attention gate block is more… More >

Seonjae Kim, Dongsan Jun^*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3267-3279, 2022, DOI:10.32604/cmc.2022.020651

Abstract Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. In order to optimize the… More >

Yudong Zhang^1,3,*, Xin Zhang^2,*, Weiguo Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1037-1058, 2021, DOI:10.32604/cmes.2021.015807

Abstract Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network for COVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed to avoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structure of which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracy of our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: This proposed ANC method is superior to 9 state-of-the-art approaches. More >

Yugang Li^{1, *}, Haibo Sun¹, Zhe Chen¹, Yudan Ding¹, Siqi Zhou²

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2529-2541, 2020, DOI:10.32604/cmc.2020.011886

Abstract Referring expressions comprehension is the task of locating the image region described by a natural language expression, which refer to the properties of the region or the relationships with other regions. Most previous work handles this problem by selecting the most relevant regions from a set of candidate regions, when there are many candidate regions in the set these methods are inefficient. Inspired by recent success of image captioning by using deep learning methods, in this paper we proposed a framework to understand the referring expressions by multiple steps of reasoning. We present a model for referring expressions comprehension by… More >