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Pattern Recognition and Artificial Intelligence  2023, Vol. 36 Issue (9): 778-792    DOI: 10.16451/j.cnki.issn1003-6059.202309002
Deep Learning Based Pattern Classification and Detection Current Issue| Next Issue| Archive| Adv Search |
Fuzzy Logic Guided Deep Neural Network with Multi-granularity
ZHOU Tianyi1, DING Weiping1, HUANG Jiashuang1, JU Hengrong1, JIANG Shu1, WANG Haipeng1
1. School of Information Science and Technology, Nantong University, Nantong 226019

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Abstract  The accurate identification and classification of pathological images are crucial for early disease detection and treatment. In the process of diagnosis, pathologists typically employ a multi-level approach, observing abnormal cell regions at various magnifications. However, existing models often extract features at a single granularity, neglecting the multi-granularity nature of cells. Therefore, a fuzzy logic guided deep neural network with multi-granularity is proposed in this paper. Firstly, multi-granularity feature extraction is conducted for cell structures at three levels of granularity-coarse, medium and fine, and thus the information in pathological tissue images is fully utilized. Additionally, to address the issue of key information redundancy during multi-granularity feature extraction, fuzzy logic theory is introduced. Multiple fuzzy membership functions are set to describe cell features from different angles. Subsequently, fuzzy operations are employed to fuse into a fuzzy universal feature. A fuzzy logic guided cross attention mechanism is designed to guide the multi-granularity features by the universal fuzzy feature. Finally, an encoder is utilized to propagate the features to all patch tokens, resulting in improved classification accuracy and robustness. Experiments demonstrate that the proposed network achieves high accuracy in the classification of pathological images.
Key wordsFuzzy Logic      Medical Image Classification      Feature Fusion      Feature Extraction      Attention Mechanism     
Received: 24 July 2023     
ZTFLH: TP18  
Fund:National Natural Science Foundation of China(No.61976120,62006128,62102199), Natural Science Foundation of Jiangsu Province(No.BK20231337), Key Program of Natural Science Research of Higher Education of Jiangsu Province(No.21KJA510004), General Program of Nature Science Research of Higher Education of Jiangsu Province(No.23KJB520031), China Postdoctoral Science Foundation(No.2022M711716)
Corresponding Authors: DING Weiping, Ph.D, professor. His research interests include data mining, machine learning, granular computing, evolutionary computing and big data analytics.)   
About author:: ZHOU Tianyi, master student. His research interests include granular computing, fuzzy sets and deep learning.HUANG Jiashuang, Ph.D., associate professor. His research interests include brain network analysis and deep learning. JU Hengrong, Ph.D., associate professor. His research interests include granular computing, rough sets, machine learning and know-ledge discovery. JIANG Shu, Ph.D., lecturer. Her research interests include deep learning and na-tural language processing. WANG Haipeng, master student. His research include fuzzy theory, granular computing and deep learning.
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ZHOU Tianyi
DING Weiping
HUANG Jiashuang
JU Hengrong
JIANG Shu
WANG Haipeng
Cite this article:   
ZHOU Tianyi,DING Weiping,HUANG Jiashuang等. Fuzzy Logic Guided Deep Neural Network with Multi-granularity[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(9): 778-792.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202309002      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I9/778
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