Innovative solutions for CNN performance: A TRIZ-based reverse engineering approach

Authors

  • Merve Cosgun Department of Industrial Engineering, Bursa Technical University, Bursa, Turkey.
  • Koray Altun Department of Industrial Engineering, Bursa Technical University, Bursa, Turkey

DOI:

https://doi.org/10.6977/IJoSI.202506_9(3).0001

Keywords:

Image classification, CNN, Reverse engineering, TRIZ

Abstract

Convolutional Neural Networks (CNNs) are widely used in computer vision for tasks like image classification and detection. These models work well when the number of image classes is small, but as the number of classes increases, accuracy tends to drop due to overfitting. There are several methods to address this issue, such as data augmentation, preprocessing, class weighting, transfer learning, and adjusting technical parameters. This study introduces a novel approach by utilizing the TRIZ methodology to systematically analyze and enhance these existing methods. Using reverse engineering, we deconstructed current solutions and aligned them with TRIZ principles to propose more innovative and effective approaches for improving CNN performance. The results show that TRIZ provides a structured and creative framework for solving accuracy decline issues in CNN models, offering potential for broader applications in other machine learning architectures.

Keywords: Image classification, CNN, Reverse engineering, TRIZ

Author Biography

Koray Altun, Department of Industrial Engineering, Bursa Technical University, Bursa, Turkey

Department of Industrial Engineering

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Published

2025-07-03

Issue

Vol. 9 No. 3 (2025): International Journal of Systematic Innovation

Section

Regular full papers

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