IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v349y2025i1d10.1007_s10479-023-05299-1.html
   My bibliography  Save this article

A fuzzy evaluation approach to determine superiority of deep learning network system in terms of recognition capability: case study of lung cancer imaging

Author

Listed:
  • Tsang-Chuan Chang

    (National Taichung University of Science and Technology)

Abstract

Artificial intelligence (AI) assists in decision-making across various fields and industries. Diverse market needs have prompted the rapid evolution of AI learning algorithms. Deep learning networks (DLNs) process classification problems associated with perceptrons; this approach has become mainstream in the current AI era. To compare the classification and recognition performances of the designed DLN systems, most studies have applied confusion matrices as assessment tools and further computed accuracy, sensitivity, and specificity indices for judgment and analysis. However, the values of these indices change with the degree of learning achieved by the network system each time it is trained. Thus, using a single index value or mean value to determine recognition capabilities may lead to misjudgment. In view of this, we used accuracy to define a recognition performance index (RPI) $$I_{ACC}$$ I ACC . Considering the unavoidable uncertainty in $$I_{ACC}$$ I ACC , we further propose a triangular fuzzy number (TFN) for $$I_{ACC}$$ I ACC . This is applied to develop a fuzzy test model for $$I_{ACC}$$ I ACC to aid researchers in evaluating superiority among the designed DLN systems in terms of recognition capabilities. To demonstrate the applicability of the proposed approach, we implemented it on a LeNet-5 convolutional neural network system optimized using the Taguchi method for tomographic images of lung cancer provided by the 2015 International Society for Optics and Photonics (SPIE).

Suggested Citation

  • Tsang-Chuan Chang, 2025. "A fuzzy evaluation approach to determine superiority of deep learning network system in terms of recognition capability: case study of lung cancer imaging," Annals of Operations Research, Springer, vol. 349(1), pages 3-23, June.
  • Handle: RePEc:spr:annopr:v:349:y:2025:i:1:d:10.1007_s10479-023-05299-1
    DOI: 10.1007/s10479-023-05299-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-023-05299-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-023-05299-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ha Che-Ngoc & Thao Nguyen-Trang & Tran Nguyen-Bao & Trung Nguyen-Thoi & Tai Vo-Van, 2022. "A new approach for face detection using the maximum function of probability density functions," Annals of Operations Research, Springer, vol. 312(1), pages 99-119, May.
    2. Bas, Javier & Cirillo, Cinzia & Cherchi, Elisabetta, 2021. "Classification of potential electric vehicle purchasers: A machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    3. Feijóo, Claudio & Kwon, Youngsun, 2020. "AI impacts on economy and society: Latest developments, open issues and new policy measures," Telecommunications Policy, Elsevier, vol. 44(6).
    4. Kuen-Suan Chen & Tsang-Chuan Chang, 2022. "Fuzzy testing model for the lifetime performance of products under consideration with exponential distribution," Annals of Operations Research, Springer, vol. 312(1), pages 87-98, May.
    5. Kuen-Suan Chen & Yuan-Lung Lai & Ming-Chieh Huang & Tsang-Chuan Chang, 2023. "Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics," International Journal of Production Research, Taylor & Francis Journals, vol. 61(5), pages 1591-1605, March.
    6. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    7. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    8. Wu, Chien-Wei, 2009. "Decision-making in testing process performance with fuzzy data," European Journal of Operational Research, Elsevier, vol. 193(2), pages 499-509, March.
    9. Tsang-Chuan Chang & Kuen-Suan Chen, 2019. "Testing process quality of wire bonding with multiple gold wires from viewpoint of producers," International Journal of Production Research, Taylor & Francis Journals, vol. 57(17), pages 5400-5413, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Scindhiya Laxmi & S. K. Gupta & Sumit Kumar, 2024. "Intuitionistic fuzzy least square twin support vector machines for pattern classification," Annals of Operations Research, Springer, vol. 339(3), pages 1329-1378, August.
    2. Mohammad Zoynul Abedin & Mahmudul Hasan Moon & M. Kabir Hassan & Petr Hajek, 2025. "Deep learning-based exchange rate prediction during the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 345(2), pages 1335-1386, February.
    3. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    4. Walid Ben Omrane & Qianru Qi & Samir Saadi, 2025. "Cryptocurrency markets, macroeconomic news announcements and energy consumption," Annals of Operations Research, Springer, vol. 347(1), pages 743-760, April.
    5. Esam Mahdi & Carlos Martin-Barreiro & Xavier Cabezas, 2025. "A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices," Mathematics, MDPI, vol. 13(9), pages 1-19, April.
    6. Kuo-Ching Chiou, 2023. "Building Up of Fuzzy Evaluation Model of Life Performance Based on Type-II Censored Data," Mathematics, MDPI, vol. 11(17), pages 1-12, August.
    7. Lamperti, Fabio, 2024. "Unlocking machine learning for social sciences: The case for identifying Industry 4.0 adoption across business restructuring events," Technological Forecasting and Social Change, Elsevier, vol. 207(C).
    8. Zhang, Ying & Gao, Kaiye & Ma, Tianyi & Wang, Huan & Li, Yan-Fu, 2024. "Intelligent recognition of structural health state of EV lithium-ion Battery using transfer learning based on X-ray computed tomography," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    9. Wei Lo & Chun-Ming Yang & Kuei-Kuei Lai & Shao-Yu Li & Chi-Han Chen, 2021. "Developing a Novel Fuzzy Evaluation Model by One-Sided Specification Capability Indices," Mathematics, MDPI, vol. 9(10), pages 1-11, May.
    10. Iván E. Villalón-Turrubiates & Rogelio López-Herrera & Jorge L. García-Alcaraz & José R. Díaz-Reza & Arturo Soto-Cabral & Iván González-Lazalde & Gerardo Grijalva-Avila & José L. Rodríguez-Álvarez, 2022. "A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE," Mathematics, MDPI, vol. 10(16), pages 1-27, August.
    11. Zhiqiang Xu & Mahdi Aghaabbasi & Mujahid Ali & Elżbieta Macioszek, 2022. "Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    12. Gholamreza Hesamian & Mohamad Ghasem Akbari, 2021. "A process capability index for normal random variable with intuitionistic fuzzy information," Operational Research, Springer, vol. 21(2), pages 951-964, June.
    13. Shuai Ma & Kechen Song & Menghui Niu & Hongkun Tian & Yunhui Yan, 2024. "Cross-scale fusion and domain adversarial network for generalizable rail surface defect segmentation on unseen datasets," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 367-386, January.
    14. Lee, Carmen Kar Hang & Leung, Eric Ka Ho, 2023. "Spatiotemporal analysis of bike-share demand using DTW-based clustering and predictive analytics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    15. Yuanyuan Wang & Ling Ma & Lihua Jian & Huiqin Jiang, 2023. "Conductive particle detection via efficient encoder–decoder network," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3563-3577, December.
    16. Chun-Min Yu & Win-Jet Luo & Ting-Hsin Hsu & Kuei-Kuei Lai, 2020. "Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index," Mathematics, MDPI, vol. 8(12), pages 1-18, November.
    17. Nagl, Maximilian, 2024. "Intricacy of cryptocurrency returns," Economics Letters, Elsevier, vol. 239(C).
    18. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    19. Swarit Anand Singh & K. A. Desai, 2023. "Automated surface defect detection framework using machine vision and convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1995-2011, April.
    20. José Almeida & Tiago Cruz Gonçalves, 2024. "Cryptocurrency market microstructure: a systematic literature review," Annals of Operations Research, Springer, vol. 332(1), pages 1035-1068, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:349:y:2025:i:1:d:10.1007_s10479-023-05299-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.