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

Tsang-Chuan Chang ()
Additional contact information
Tsang-Chuan Chang: National Taichung University of Science and Technology

Annals of Operations Research, 2025, vol. 349, issue 1, No 2, 3-23

Abstract: 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).

Keywords: Deep learning network system; Taguchi method; Recognition performance index; Triangular fuzzy number; Fuzzy testing; Lung cancer (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05299-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:349:y:2025:i:1:d:10.1007_s10479-023-05299-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05299-1

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().