Browsing by Author "Aslan, E."

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Citation - Scopus: 1
Alzheimer’s Classification with a MaxViT-Based Deep Learning Model Using Magnetic Resonance Imaging
(Interdisciplinary Publishing Academia, 2025) Demirtaş Alpsalaz, S.; Aslan, E.; Özüpak, Y.; Alpsalaz, F.; Uzel, H.
Alzheimer’s disease (AD), a progressive neurodegenerative disorder, poses significant challenges for early diagnosis due to subtle symptom onset and overlap with normal aging. This study aims to develop an effective deep learning model for classifying four AD stages (Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented) using brain MRI scans. We propose a Multi-Axis Vision Transformer (MaxViT)-based framework, leveraging transfer learning and robust data augmentation on the Kaggle Alzheimer’s MRI Dataset to address class imbalance and enhance generalization. The model employs MaxViT’s multi-axis attention mechanisms to capture both local and global patterns in MRI images. Our approach achieved a classification accuracy of 99.60%, with precision of 99.0%, recall of 98.1%, and F1-score of 98.51%. These results highlight MaxViT’s superior ability to differentiate AD stages, particularly in distinguishing challenging early stages. The proposed model offers a reliable tool for early AD diagnosis, laying a strong foundation for future clinical applications and interdisciplinary research in neurodegenerative disease detection. Future work should explore larger, more diverse datasets and additional biomarkers to further validate and enhance model performance. © 2025 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Development of Malaria Diagnosis With Convolutional Neural Network Architectures: a Cnn-Based Software for Accurate Cell Image Analysis
(Galileo Institute of Technology and Education of the Amazon (ITEGAM), 2025) Aslan, E.
This study emphasizes that early diagnosis and treatment of malaria is critical in reducing health problems and mortality from the disease, especially in developing countries where the disease is prevalent. Malaria is a potentially fatal disease transmitted to humans by mosquitoes infected by a blood parasite called Plasmodium. The traditional method of diagnosis relies on experts examining red blood cells under a microscope and is inefficient as it is dependent on expert knowledge and experience. Nowadays, machine learning methods that provide high accuracy are increasingly used in disease detection. In this paper, a Convolutional Neural Network (CNN) architecture is proposed to distinguish between parasitized and non-parasitized cells. In addition, the performance of the proposed CNN architecture is compared to pre-trained CNN models such as VGG-19 and EfficientNetB3. The studies were carried out using the Malaria Dataset supplied by the National Institute of Health (NIH), and our proposed architecture was shown to function with 99.12% accuracy. The results of the study reveal that it is effective in improving the accuracy of cell images containing Plasmodium. In addition, a software that predicts whether cell images are noisy or not has been developed. © 2025 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM).
Improving Accuracy Through Preprocessing and Data Augmentation Techniques with a Deep Learning-Based Approach for Arrhythmia Detection
(Penerbit UTHM, 2025) Aslan, E.; Özüpak, Y.
Arrhythmia detection plays a critical role in the early diagnosis and management of cardiovascular diseases. In this study, we propose a deep learning-based model for arrhythmia classification using advanced preprocessing and data augmentation techniques. The proposed model is evaluated on the MIT-BIH Arrhythmia Dataset and the PTB Diagnostic ECG Dataset and achieves 98% and 95% accuracy rates, respectively. These results demonstrate the strong ability of the model to classify complex heartbeat patterns, achieving higher accuracy, precision, sensitivity, and F1 score compared to existing methods. The model uses a convolutional neural network (CNN) architecture trained on pre-processed ECG signals with data segmented into individual heartbeats. Data augmentation techniques are applied to reduce data imbalances and improve the generalization ability of the model. Experimental results highlight that the model provides a significant increase in accuracy rates over traditional methods. The results of this study highlight the potential of deep learning architectures in biomedical signal analysis, especially for real-time arrhythmia detection. This approach offers promising potential for clinical applications by enabling higher diagnostic accuracy and timely intervention in cardiovascular healthcare. © This is an open access article under the CC BY-NC-SA 4.0 license.