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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Intelligent Informatics
Ku Ruhana Ku-Mahamud
Universiti Muhammadiyah Malaysia
Computer Science & IT Engineering

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Comparative analysis of xception and svm for brain tumor classification on mri images Nurul Huda; Ku Ruhana Ku-Mahamud
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2016

Abstract

Brain tumor classification from magnetic resonance imaging (MRI) plays a critical role in supporting radiologists during diagnosis and treatment planning. However, many existing automated approaches employ limited preprocessing, single-stage transfer learning, or evaluation on a single dataset, which restricts robustness and clinical applicability. This study proposes an enhanced transfer-learning framework based on the Xception architecture for multiclass brain tumor classification and compares its performance with baseline models under identical experimental conditions. The framework integrates a comprehensive preprocessing pipeline consisting of normalization, adaptive noise filtering, contrast enhancement, and targeted data augmentation, together with a structured two-phase fine-tuning strategy. A total of 6,537 MRI images were used, employing five-fold cross-validation, independent testing, and validation on an additional benchmark dataset. The proposed model achieved a mean cross-validation accuracy of 0.8994 ± 0.089 and 99.06% accuracy, precision, and recall on the independent test set, demonstrating strong stability and generalization ability. Evaluation on the CE-MRI Figshare dataset further confirmed robustness, yielding 98.45% accuracy, 98% precision, and 98% recall. In contrast, when re-evaluated in the same experimental setting, baseline models performed considerably worse: the SVM classifier achieved 21.41% accuracy, and ResNet50 reached 75.27%, both substantially lower than Xception. Although higher accuracies for these models have been reported in prior studies under different conditions, the present findings highlight their limited generalization under unified evaluation. Overall, the proposed Xception-based framework provides a reliable and generalizable solution for automated brain tumor classification, with strong potential to support clinical workflows such as triage prioritization and second-opinion assistance.
Hyperparameter optimization of deep residual recurrent fusion models for facial emotion recognition Muhammad Munsarif; Ku Ruhana Ku-Mahamud
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.pp2581-2594

Abstract

Deep learning facial emotion recognition (FER) is widely applied in healthcare, education, and human–computer interaction. However, many deep learning models suffer from suboptimal hyperparameter configurations that reduce accuracy and stability. This study proposes three deep residual recurrent fusion models that integrates residual blocks with recurrent neural networks (bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), and gated recurrent unit (GRU)) to capture both spatial and temporal features. A systematic hyperparameter optimization strategy was applied, tuning kernel size, filter size, recurrent units, batch size, learning rate, dropout, and weight decay to balance generalization and computational efficiency. The models were evaluated on four benchmark datasets: FER2013, FERPlus, RAF-DB, and CK+. The results show that optimized configurations achieved outstanding accuracy, reaching 99.85% on FER2013, 99.99% on FERPlus, and 100% on RAF-DB and CK+. These findings demonstrate that careful hyperparameter tuning significantly enhances feature extraction, mitigates vanishing gradient and overfitting issues, and improves generalization across diverse datasets. The proposed framework highlights the importance of optimization in advancing robust FER systems for real-world applications.
Co-Authors Muhammad Munsarif Nurul Huda