Browsing by Author "Aslan, E."

Now showing 1 - 3 of 3

Attention Enhanced Inceptionnext-Based Hybrid Deep Learning Model for Lung Cancer Detection
(Institute of Electrical and Electronics Engineers Inc., 2025) Ozdemir, B.; Aslan, E.; Pacal, I.
Lung cancer is the most common cause of cancer-related mortality globally. Early diagnosis of this highly fatal and prevalent disease can significantly improve survival rates and prevent its progression. Computed tomography (CT) is the gold standard imaging modality for lung cancer diagnosis, offering critical insights into the assessment of lung nodules. We present a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs). By optimizing and integrating grid and block attention mechanisms with InceptionNeXt blocks, the proposed model effectively captures both fine-grained and large-scale features in CT images. This comprehensive approach enables the model not only to differentiate between malignant and benign nodules but also to identify specific cancer subtypes such as adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. The use of InceptionNeXt blocks facilitates multi-scale feature processing, making the model particularly effective for complex and diverse lung nodule patterns. Similarly, including grid attention improves the model's capacity to identify spatial relationships across different sections of the picture, whereas block attention focuses on capturing hierarchical and contextual information, allowing for precise identification and categorization of lung nodules. To ensure robustness and generalizability, the model was trained and validated using two public datasets, Chest CT and IQ-OTH/NCCD, employing transfer learning and pre-processing techniques to improve detection accuracy. The proposed model achieved an impressive accuracy of 99.54% on the IQ-OTH/NCCD dataset and 98.41% on the Chest CT dataset, outperforming state-of-the-art CNN-based and ViT-based methods. With only 18.1 million parameters, the model provides a lightweight yet powerful solution for early lung cancer detection, potentially improving clinical outcomes and increasing patient survival rates. © 2013 IEEE.
Comparison of Machine Learning Algorithms for Automatic Prediction of Alzheimer Disease
(Wolters Kluwer Health, 2025) Aslan, E.; Özüpak, Y.
Background: Alzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic tools, such as intracranial volume assessments, electroencephalography (EEG) signals, and brain magnetic resonance imaging (MRI), have shown utility in detecting the disease. However, artificial intelligence (AI) offers promise for automating this process, potentially enhancing diagnostic accuracy and accessibility. Methods: In this study, various machine learning models were used to detect Alzheimer disease, including K-nearest neighbor regression, support vector machines (SVM), AdaBoost regression, and logistic regression. A neural network was constructed and validated using data from 150 participants in the University of Washington's Alzheimer's Disease Research Center (Open Access Imaging Studies Series [OASIS] dataset). Cross-validation was also performed on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset to assess the robustness of the models. Results: Among the models tested, K-nearest neighbor regression achieved the highest accuracy, reaching 97.33%. The cross-validation on the ADNI dataset further confirmed the effectiveness of the models, demonstrating satisfactory results in screening and diagnosing Alzheimer disease in a community-based sample. Conclusion: The findings indicate that AI-based models, particularly K-nearest neighbor regression, provide promising accuracy for the early detection of Alzheimer disease. This approach has potential for further development into practical diagnostic tools that could be applied in clinical and community settings. © 2024, the Chinese Medical Association. This is an open access article under the CC BY-NC-ND license.
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).