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| Pneumonia Classification and Recognition Method Based on Multi-resolution Attention Dense Network |
| ZHOU Tao1,2, YE Xinyu1,2, LU Huiling3, CHANG Xiaoyu1,2, LIU Yuncan1,2 |
1. Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021;
2. Department of Computer Science and Engineering, North Minzu University, Yinchuan 750021;
3. Department of Science, Ningxia Medical University, Yinchuan 750004 |
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Abstract X-ray film of pneumonia suffers from inconspicuous imaging features, low contrast between lesions and surrounding tissues, and blurred edges. Therefore, a pneumonia classification and recognition method based on multi-resolution attention dense network is proposed. Shallow localization information and deep semantic information are deeply fused. A multi-resolution spatial attention gate is constructed to enhance semantic interaction between deep and shallow information at different resolutions, establishing interdependency for lesion information in deep and shallow information. In addition, the coordinate frequency attention is designed to adaptively enhance the representation of pneumonia features in a complementary manner of orientation and location. Experiments on five pneumonia X-ray datasets including ChestXRay2017 show that the proposed method achieves better performance in pneumonia classification and recognition task with robustness on the public pneumonia dataset.
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Received: 04 April 2023
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| Fund:National Natural Science Foundation of China(No.62062003), Ningxia Natural Science Foundation Project(No.2022AAC03149), Key Research and Development Project of Ningxia Autonomous Region(No.2020BEB04022) |
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Corresponding Authors:
ZHOU Tao, Ph.D., professor. His research interests include medical image processing, deep learning and pattern recognition.
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| About author:: About Author:YE Xinyu, master student. His research interests include medical image processing and computer-aided diagnosis.LU Huiling, master, associate professor. Her research inte-rests include image processing.CHANG Xiaoyu, master student. Her research interests include medical image processing.LIU Yuncan, master student. Her research interests include medical image processing. |
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[1] SHEN Y C, TIAN Z J, LU D F, et al. Impact of Pneumonia and Lung Cancer on Mortality of Women with Hypertension. Scientific Reports, 2016, 6(1). DOI: 10.1038/s41598-016-0023-2.
[2] ÇALLI E, SOGANCIOGLU E, VAN GINNEKEN B,et al. Deep Learning for Chest X-Ray Analysis: A Survey. Medical Image Ana-lysis, 2021, 72. DOI: 10.1016/j.media.2021.102125.
[3] SHARMA S, GULERIA K.A Deep Learning Based Model for the Detection of Pneumonia from Chest X-Ray Images Using VGG-16 and Neural Networks. Procedia Computer Science, 2023, 218: 357-366.
[4] BHANDARY A, PRABHU G A, RAJINIKANTH V, et al. Deep-Learning Framework to Detect Lung Abnormality-A Study with Chest X-Ray and Lung CT Scan Images. Pattern Recognition Le-tters, 2020, 129: 271-278.
[5] BANERJEE T, BATTA D, JAIN A, et al. Deep Belief Convolutio-nal Neural Network with Artificial Image Creation by GANs Based Diagnosis of Pneumonia in Radiological Samples of the Pectoralis Major//Proc of Innovations in Electrical and Electronic Engineering. Berlin, Germany: Springer, 2021: 979-1002.
[6] AFSHAR P, HEIDARIAN S, NADERKHANI F, et al. COVID-CAPS: A Capsule Network-Based Framework for Identification of COVID-19 Cases from X-Ray Images. Pattern Recognition Letters, 2020, 138: 638-643.
[7] LIU J, QI J, CHEN W, et al. Multi-branch Fusion Auxiliary Lear-ning for the Detection of Pneumonia from Chest X-Ray Images. Computers in Biology and Medicine, 2022, 147. DOI: 10.1016/j.compbiomed.2022.105732.
[8] ZHOU T, YE X Y, LU H L, et al. Dense Convolutional Network and Its Application in Medical Image Analysis. BioMed Research International, 2022. DOI: 10.1155/2022/2384830.
[9] HAMMOUDI K, BENHABILES H, MELKEMI M, et al. Deep Learning on Chest X-Ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19. Journal of Medical Systems, 2021, 45(7). DOI: 10.1007/s10916-021-01745-4.
[10] QIN R X, WANG Z Z, JIANG L Y, et al. Fine-Grained Lung Cancer Classification from PET and CT Images Based on Multidimensional Attention Mechanism. Complexity, 2020. DOI: 10.1155/2020/6153657.
[11] CHEN B L, ZHAO T S, LIU J H, et al. Multipath Feature Recalibration DenseNet for Image Classification. International Journal of Machine Learning and Cybernetics, 2021, 12: 651-660.
[12] ZHANG J, ZHENG B W, GAO A, et al. A 3D Densely Connected Convolution Neural Network with Connection-Wise Attention Mecha-nism for Alzheimer's Disease Classification. Magnetic Resonance Imaging, 2021, 78: 119-126.
[13] PARK S, KIM G, OH Y, et al. Multi-task Vision Transformer Using Low-Level Chest X-Ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification. Medical Image Analysis, 2022, 75. DOI: 10.1016/j.media.2021.102299.
[14] UKWUOMA C C, QIN Z G, HEYAT M B B, et al. A Hybrid Explainable Ensemble Transformer Encoder for Pneumonia Identification from Chest X-Ray Images. Journal of Advanced Research, 2022, 48: 191-211.
[15] OKOLO G I, KATSIGIANNIS S, RAMZAN N. IEViT: An Enhanced Vision Transformer Architecture for Chest X-Ray Image Classification. Computer Methods and Programs in Biomedicine, 2022, 226. DOI: 10.1016/j.cmpb.2022.107141.
[16] UPARKAR O, BHARTI J, PATERIYA R K, et al. Vision Transformer Outperforms Deep Convolutional Neural Network-Based Model in Classifying X-Ray Images. Procedia Computer Science, 2023, 218: 2338-2349.
[17] PENG Z L, GUO Z H, HUANG W, et al. Conformer: Local Features Coupling Global Representations for Recognition and Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. DOI: 10.1109/TPAMI.2023.3243048.
[18] ZHAO H S, SHI J P, QI X J, et al. Pyramid Scene Parsing Network//Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2017: 6230-6239.
[19] KERMANY D, ZHANG K, GOLDBAUM M.Labeled Optical Coherence Tomography(OCT) and Chest X-Ray Images for Classification. Mendeley Data, 2018, 2(2). DOI: 10.17632/rscbjbr9sj.2.
[20] ELARABY M E, ELZEKI O M, SHAMS M Y, et al. A Novel Gray-Scale Spatial Exploitation Learning Net for COVID-19 by Crawling Internet Resources. Biomedical Signal Processing and Control, 2022, 73. DOI: 10.1016/j.bspc.2021.103441.
[21] CHETOUI M, AKHLOUFI M A.Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-Rays. Journal of Clinical Medicine, 2022, 11(11). DOI: 10.3390/jcm11113013.
[22] BALASUBRAMANIAN K, ANANTHAMOORTHY N P, RAMYA K. An End-End Deep Learning Framework for Lung Infection Re-cognition Using Attention Based Features and Cross Average Pooling. International Journal for Multiscale Computational Enginee-ring, 2022, 20(2): 67-82.
[23] 张智睿,李锵,关欣.密集挤压激励网络的多标签胸部X 光片疾病分类.中国图象图形学报, 2020, 25(10): 2238-2248.
(ZHANG Z R, LI Q, GUAN X.Multilabel Chest X-Ray Disease Classification Based on a Dense Squeeze-and-Excitation Network. Journal of Image and Graphics, 2020, 25(10): 2238-2248.)
[24] RADOSAVOVIC I, KOSARAJU R P, GIRSHICK R, et al. Designing Network Design Spaces[C/OL].[2023-03-12]. http://arxiv.org/pdf/2023.13678.pdf.
[25] LIU Z, MAO H Z, WU C Y, et al. A ConvNet for the 2020s//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 11966-11976.
[26] LIU Z, LIN Y T, CAO Y, et al. Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows//Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2021: 9992-10002.
[27] YU W H, LUO M, ZHOU P, et al. MetaFormer Is Actually What You Need for Vision//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 10809-10819.
[28] LI J S, XIA X, LI W, et al. Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios[C/OL].[2023-03-12]. https://arxiv.org/abs/2207.05501.pdf.
[29] MURUGAN R, GOEL T.E-DiCoNet: Extreme Learning Machine Based Classifier for Diagnosis of COVID-19 Using Deep Convolutional Network. Journal of Ambient Intelligence and Humanized Computing, 2021, 12: 8887-8898.
[30] KHAN A I, SHAH J L, BHAT M M.CoroNet: A Deep Neural Network for Detection and Diagnosis of COVID-19 from Chest X-Ray Images. Computer Methods and Programs in Biomedicine, 2020, 196. DOI: 10.1016/j.cmpb.2020.105581. |
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2023, Vol. 36 
