Detecting and classifying palladium nanoparticles in microscopic images using neutrosophic deep learning
| dc.contributor.author | Dr. Mohamed El-dosuky | |
| dc.contributor.author | Prof. Aboul Ella Hassanien | |
| dc.contributor.author | Prof. Heba Alshater | |
| dc.contributor.author | Mr. Rania Ahmed | |
| dc.contributor.author | Mr. ameh H. Basha | |
| dc.contributor.author | Dr. Heba AboulElla | |
| dc.contributor.author | Mr. Ashraf Darwish | |
| dc.contributor.author | Prof. Sara Abdelghafar | |
| dc.date.accessioned | 2026-05-20T15:27:21Z | |
| dc.date.issued | 2026-02-15 | |
| dc.description.abstract | detection and classification of palladium nanoparticles in scanning electron microscopy (SEM) images, distinguishing between ordered and disordered structures for accurate nanoparticle characterization. The model follows a five-phase pipeline for enhanced accuracy and efficiency. It begins with data augmentation, applying transformations like rotation and flipping to improve dataset diversity. The second phase uses neutrosophic image segmentation to manage uncertainty and noise in SEM images, allowing for the precise isolation of nanoparticle regions. In the third phase, the VGG-19 deep neural network extracts high-level features, initially identifying 25,088 features. In the fourth phase, a hybrid approach combining Gini importance and Genetic Optimized Rough Sets (GORS) reduces the number of features to 2454. The refined feature set is then classified using a Random Forest classifier, which effectively distinguishes between ordered and disordered palladium nanoparticles. To validate its performance, the proposed model was evaluated on a dataset of 1000 SEM images of carbon-based materials with deposited palladium nanoparticles, which was then expanded to 1500 images to address class imbalance and minimize overfitting. The experimental results highlight the model's strong potential as a high-performance classification tool for nanoparticle analysis in SEM images, achieving an overall accuracy of 99.67 %. To evaluate the impact of the introduced phases on the proposed model's performance, four ablation experiments were conducted, demonstrating the significance of each phase. Dropping data augmentation and feature reduction reduced accuracy approximately to 97.5 %, while dropping the feature extraction phase reduced it further to 94.17 %, highlighting the critical impact of these processes on performance and robustness. | |
| dc.identifier.uri | https://research.arabeast.edu.sa/handle/123456789/1138 | |
| dc.language.iso | en | |
| dc.publisher | Chemometrics and Intelligent Laboratory Systems | |
| dc.title | Detecting and classifying palladium nanoparticles in microscopic images using neutrosophic deep learning | |
| dc.title.alternative | Detecting and classifying palladium nanoparticles in microscopic images using neutrosophic deep learning | |
| dc.type | Article |