Effective Feature Selection Technique for Deep Learning-Based Weld Defect Classification in Gamma Radiographic Images
Elsharkawy, Z., Fikry, R., Hammad, M., Kasban, H. (2025). Effective Feature Selection Technique for Deep Learning-Based Weld Defect Classification in Gamma Radiographic Images. EKB Journal Management System, 34(2), 67-77. doi: 10.21608/mjeer.2025.393566.1153
Zeinab F. Elsharkawy; Refaat M Fikry; M. E. Hammad; Hani Kasban. "Effective Feature Selection Technique for Deep Learning-Based Weld Defect Classification in Gamma Radiographic Images". EKB Journal Management System, 34, 2, 2025, 67-77. doi: 10.21608/mjeer.2025.393566.1153
Elsharkawy, Z., Fikry, R., Hammad, M., Kasban, H. (2025). 'Effective Feature Selection Technique for Deep Learning-Based Weld Defect Classification in Gamma Radiographic Images', EKB Journal Management System, 34(2), pp. 67-77. doi: 10.21608/mjeer.2025.393566.1153
Elsharkawy, Z., Fikry, R., Hammad, M., Kasban, H. Effective Feature Selection Technique for Deep Learning-Based Weld Defect Classification in Gamma Radiographic Images. EKB Journal Management System, 2025; 34(2): 67-77. doi: 10.21608/mjeer.2025.393566.1153

Effective Feature Selection Technique for Deep Learning-Based Weld Defect Classification in Gamma Radiographic Images

Menoufia Journal of Electronic Engineering Research
Volume 34, Issue 2, July 2025, Page 67-77  XML PDF (1.08 MB)
Document Type: Original Article
DOI: 10.21608/mjeer.2025.393566.1153
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Authors
Zeinab F. Elsharkawy email orcid 1; Refaat M Fikry2; M. E. Hammad2; Hani Kasbanorcid 3
1Engineering Department, Nuclear Research Center, Egyptian Atomic Energy Authority (EAEA), Egypt.
2Engineering Department, Nuclear Research Center, Egyptian Atomic Energy Authority (EAEA), Egypt
3Engineering Department, Nuclear Research Center, Egyptian Atomic Energy Authority (EAEA), Egypt
Abstract
The traditional defect detection techniques have poor detection accuracy and are strongly influenced by the industrial imaging environment. Concentrating on this shortcoming, this study presented a hybrid Deep Learning (DL) approach for automatic weld defects classification in Gamma Radiography Images (GRIs). We focus on improving accuracy by fusion and selection of deep-learned features extracted from five different DL models (e.g. SqueezeNet, GoogleNet, ShuffleNet, DarkNet19 and MobileNet-V2). To extract robust features from the DL models, the Pearson Correlation Coefficient (PCC), F-score (FS), and ReliefF (RF) feature selection algorithms are evaluated. The RF algorithm achieved the best result. The selected features are used for classification tasks using a Multiclass K-Nearest Neighbors (MKNN) classifier. Eight main types of weld defects and the normal type are considered in the utilized RGIs dataset. Several experiments are performed using the traditional feature extraction methods, DL methods, feature selection algorithms and the proposed one. Their results are compared to evaluate the performance of the presented system. Several RF feature subsets were tested, and the 450-feature subset with the best classification performance was found. The results confirmed that the suggested strategy performs better than all traditional and DL methods with an overall classification accuracy (CA) of 99.75%.
Keywords
Deep learning; Gamma Radiography; NDT; Weld Defects; The ReliefF
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