Browsing by Author "Poyraz, Ahmet Kursad"

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Brainnext: Novel Lightweight CNN Model for the Automated Detection of Brain Disorders Using MRI Images
(Springer, 2025) Poyraz, Melahat; Poyraz, Ahmet Kursad; Dogan, Yusuf; Gunes, Selva; Mir, Hasan S.; Paul, Jose Kunnel; Acharya, Rajendra
The main aim of this study is to propose a novel convolutional neural network, named BrainNeXt, for the automated brain disorders detection using magnetic resonance images (MRI) images. Furthermore, we aim to investigate the performance of our proposed network on various medical applications. To achieve high/robust image classification performance, we gathered a new MRI dataset belonging to four classes: (1) Alzheimer's disease, (2) chronic ischemia, (3) multiple sclerosis, and (4) control. Inspired by ConvNeXt, we designed BrainNeXt as a lightweight classification model by incorporating the structural elements of the Swin Transformers Tiny model. By training our model on the collected dataset, a pretrained BrainNeXt model was obtained. Additionally, we have suggested a feature engineering (FE) approach based on the pretrained BrainNeXt, which extracted features from fixed-sized patches. To select the most discriminative/informative features, we employed the neighborhood component analysis selector in the feature selection phase. As the classifier for our patch-based FE approach, we utilized the support vector machine classifier. Our recommended BrainNeXt approach achieved an accuracy of 100% and 91.35% for training and validation. The recommended model obtained the test classification accuracy of 94.21%. To further improve the classification performance, we suggested a patch-based DFE approach, which achieved a test accuracy of 99.73%. The obtained results, surpassing 90% accuracy on the test dataset, demonstrate the effectiveness and high classification performance of the proposed models.
Flexilpq: Automated Osteoid Osteoma Detection Using Computed Tomography
(Springer London Ltd, 2026) Key, Sefa; Agar, Anil; Sercek, Ilknur; Poyraz, Ahmet Kursad; Baygin, Mehmet; Dogan, Sengul; Tuncer, Turker
In this study, we introduce FlexiLPQ, an innovative image classification model developed as the feature-engineering counterpart of FlexiViT, and evaluate its performance on Osteoid Osteoma diagnosis. For this purpose, a new Osteoid Osteoma CT dataset was curated. Using this dataset, our goal was to design an automatic detection system capable of identifying Osteoid Osteoma with high accuracy. The proposed FlexiLPQ model operates through five main phases: (i) multi-patch feature extraction using local phase quantization (LPQ), (ii) feature selection via cumulative weighted iterative neighborhood component analysis (CWINCA), (iii) classification using a t-algorithm-based k-nearest neighbors (tkNN) classifier, (iv) iterative majority voting (IMV) to refine the decision, and (v) selection of the best final outcome. FlexiLPQ was applied to the curated CT dataset and achieved a 98.89% classification accuracy. Additionally, multiple patch sizes were incorporated, and their performance differences were analyzed. The results clearly show that FlexiLPQ is an effective and robust image classification framework, and it is well suited for biomedical imaging tasks such as Osteoid Osteoma detection.