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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Maysaloon Abed Qasim
Northern Technical University
Computer Science & IT Engineering

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Stacking ensemble techniques for automated peripheral blood cell classification using Inception v3 features Marwa Mawfaq Mohamedsheet Al-Hatab; Maysaloon Abed Qasim; Nawar A. Sultan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2247-2259

Abstract

Robust distinction of blood cells is crucial in clinical evaluation. Manual examination is slow and exposed to errors. This work investigates using machine learning (ML) techniques for automated classification of eight categories of peripheral blood cell types from multi-color images. The Inception v3 network was used to extract features, a split of 66%/34% were used to evaluate the model along with 20-fold cross-validation. To reduce computational complexity, principal component analysis (PCA) was used to reduce the 2048-dimensional feature vectors to 100 components. Among all classifiers used, the highest performance without using PCA was achieved using the support vector machine (SVM) with an accuracy equal to 93.4% and an area under the curve (AUC) of 0.996. Using PCA, affected monocytes and immature granulocytes most due to the slight reduction in the accuracy and AUC which became 90.1% and to 0.991 respectively. Results were further enhanced when a stacked ensemble of neural network (NN), logistic regression (LR), and SVM were used, achieving an accuracy of 95.2% and an AUC of 0.998. The obtained findings confirmed the effectiveness of using stacked ensembles in providing a robust, high accuracy framework for automated blood cell classification, while PCA efficiently reduced dimensions with minimal performance loss.
Optimizing blood cell classification: evaluating feature dimensionality and validation strategies Ruaa H. Ali Al-Mallah; Marwa Mawfaq Mohamedsheet Al-Hatab; Maysaloon Abed Qasim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27269

Abstract

Manual blood cell classification is time consuming and may lead to inconsistent results. This study aims to assist pathologists in diagnosing hematological disorders using machine learning (ML) techniques for automated classification of blood cells in multi-color test images, distinguishing red blood cells (RBCs) and white blood cells (WBCs). Features were extracted using the InceptionV3 network, and several ML models were evaluated for classifying blood cells into eight categories. Two validation strategies: a 66%–34% train–test split and 20-fold cross-validation were applied. The effect of dimensionality reduction through principal component analysis (PCA) was also examined, reducing the feature space from 2,048 to 100 components. Among all models, support vector machine (SVM) achieved highest performance, with 93.4% accuracy and an area under the curve (AUC) of 0.996 without PCA, and 90.1% accuracy with an AUC of 0.991 after PCA. Although PCA slightly reduced accuracy, it improved computational efficiency. Overall, SVM provided the most accurate, stable, and generalizable classification results for automated blood cell analysis.
Deep feature-based multi-class Alzheimer’s disease classification with statistical performance evaluation Maysaloon Abed Qasim; Marwa Mawfaq Mohamedsheet Al-Hatab; Lubab H. Albak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp695-706

Abstract

This study evaluated the performance of multiple machine learning classifiers for the classification of Alzheimer’s disease (AD) stages using deep features extracted from a pre-trained SqueezeNet model. Magnetic resonance imaging (MRI) scans were processed through SqueezeNet to generate high-dimensional feature vectors, which were then used as achieved an accuracy of 94.78% input to six classifiers: k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), neural network (NN), naive Bayes (NB), and logistic regression (LR). Models were assessed using a 70/30% training-testing split and 5-, 10-, and 20-fold stratified cross validation. Principal component analysis (PCA) was applied to retain 99% of variance. On the original dataset consisting of 6,400 images, KNN has achieved 97.48% accuracy and 0.998 area under the curve (AUC), and when a larger dataset of 44,000 images was used it achieved an accuracy and of 94.78% and an AUC of 0.987, demonstrating the system’s robustness across scales. Statistical tests, including paired t-tests and Wilcoxon signed-rank tests, confirmed that KNN has significantly leveraged from PCA. These outcomes demonstrate that combining deep feature extraction with PCA improved the reliability and efficiency of the classifier for AD stage prediction.
Co-Authors Lubab H. Albak Marwa Mawfaq Mohamedsheet Al-Hatab Nawar A. Sultan Ruaa H. Ali Al-Mallah