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All Journal Bulletin of Electrical Engineering and Informatics
Sabura Banu Urundai Meeran
Saveetha Engineering College
Electrical & Electronics Engineering

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Osprey optimization algorithm for VGG16 hyperparameter optimization in breast cancer detection Sabura Banu Urundai Meeran; Nafeena Abdul Munaf; Vengadeshwaran Velu
Bulletin of Electrical Engineering and Informatics 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/eei.v15i3.11181

Abstract

Globally, breast cancer is one of the reason for mortality among women and accurate automated diagnosis remains a critical research challenge. This research is used to improve breast cancer classification performance by optimizing deep learning (DL) model hyperparameters using a bio-inspired optimization technique. The osprey optimization algorithm (OOA) is applied to fine-tune the hyperparameters of the VGG16 convolutional neural network (CNN) for histopathological breast cancer image classification. The optimized model is evaluated using a curated dataset and compared with established DL architectures, including AlexNet, Xception, InceptionV3, and ResNet50. Performance is assessed using standard evaluation metrics such as accuracy, precision, recall, F1-score, specificity, AUC-ROC, Matthews correlation coefficient (MCC), log loss, and inference time. Experimental results indicate that the OOA-optimized VGG16 model achieves superior performance, with an accuracy of 97.7%, precision of 96.71%, recall of 97.79%, AUC-ROC of 99.92%, and MCC of 0.9449, while maintaining competitive computational efficiency. The results demonstrate that bio-inspired hyperparameter optimization significantly enhances classification reliability and diagnostic accuracy. In summary, integrating OOA optimization with the VGG16 architecture yields a dependable framework for breast cancer identification, making it a promising candidate for deployment in automated diagnostic support systems.
Comparative analysis of unidirectional and bidirectional RNNs for ECG arrhythmia detection using augmented MIT-BIH data Sabura Banu Urundai Meeran; Nafeena Abdul Munaf
Bulletin of Electrical Engineering and Informatics 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/eei.v15i1.10893

Abstract

Accurate classification of electrocardiogram (ECG) signals is essential for early arrhythmia detection. This study compares the performance of unidirectional and bidirectional recurrent neural networks (RNN), specifically gated recurrent unit (GRU)-based architectures, for classifying ECG beats as normal or arrhythmic. ECG data were sourced from the MIT-BIH Arrhythmia Database using the WFDB toolkit. Each beat was segmented into a 128-sample window centered on the R-peak and labeled into two classes. To address severe class imbalance (6,279 normal vs. 43 arrhythmic beats), data augmentation techniques—jittering and scaling—were applied, resulting in a balanced dataset. Both models were trained under identical conditions, with evaluation based on accuracy, precision, recall, F1-score, and other statistical metrics. The unidirectional RNN achieved poor recall (9.0%) despite high precision, yielding an overall accuracy of 54.0%. In contrast, the bidirectional RNN significantly outperformed, achieving 98.17% accuracy, 98.39% precision, 97.92% recall, and a 98.16% F1-score. The results demonstrate that bidirectional temporal modeling provides substantial improvements in ECG classification, especially for detecting minority class arrhythmias. This study highlights the importance of both data augmentation and model architecture in developing effective deep learning solutions for real-time ECG analysis and clinical diagnostics.
Co-Authors Nafeena Abdul Munaf Vengadeshwaran Velu