Browsing by Author "Yeganli, S. Faegheh"

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Citation - WoS: 3
Citation - Scopus: 3
Fast and Interpretable Deep Learning Pipeline for Breast Cancer Recognition
(IEEE, 2022) Bonyani, Mahdi; Yeganli, Faezeh; Yeganli, S. Faegheh
Breast cancer is one of the main causes of death across the world in women. Early diagnosis of this type of cancer is critical for treatment and patient care. In this paper, we propose a fast and interpretable deep learning-based pipeline for automatic detection of the metastatic tissues in breast histopathological images. Firstly, the proposed pipeline uses multiple preprocessing and data augmentation techniques to reduce over-fitting. Then, the proposed pipeline employs one - cycle policy technique in the pre-trained convolutional neural networks model in shallow and deep fine-tuning phases to find the optimal values. Finally, gradient-weighted class activation mapping (Grad-CAM) technique is utilized to produce a coarse localization map of the important regions in the image. Experiments on the PatchCamelyon dataset demonstrate the superior classification performance of the proposed method over the state-of-the-art.
Unified Deep Learning Method for Accurate Brain Tumor Segmentation Using Vertical Voxel Grouping and Wavelet Features
(Springer London Ltd, 2025) Sahin, M. Faruk; Yeganli, S. Faegheh; Uludag, Goenuel; Yeganli, Faezeh; Anka, Ferzat
Brain tumor segmentation plays a vital role in medical imaging, enabling accurate diagnosis and guiding treatment decisions. Despite notable progress driven by deep neural networks (DNNs) and multi-parametric magnetic resonance imaging (mpMRI), the complexity and heterogeneity of tumor tissues make precise segmentation a persistent challenge. In this paper, we propose a novel method that integrates Vertically grouped Voxel Feature Extraction (VFE), wavelet-based multi-resolution detail enhancement, and a modified UNet-VGG16+ architecture. The VFE component enhances tumor region contrast and suppresses irrelevant background areas by grouping column-wise voxel intensities within each slice. As a result, the average image contrast is increased by 23.78%, thereby improving the ability of Deep Neural Networks (DNNs) to focus on tumor regions. The wavelet-based enhancement captures multi-resolution details to more clearly delineate tumor boundaries while also reducing noise. The UNet-VGG16+ architecture leverages transfer learning to efficiently process these enhanced features for accurate segmentation. Extensive experiments on the BraTS21 dataset demonstrate that the proposed method achieves a mean Dice score of 94.69%, with segmentation accuracies of 93.3%, 93.1%, and 94.4% for Enhancing Tumor (ET), Whole Tumor (WT), and Tumor Core (TC), respectively. Comparative evaluations show consistent and statistically significant improvements over state-of-the-art models (p< 0.001). Further validation on the BraTS18 dataset confirms the model's generalizability. These results highlight the effectiveness of combining spatially structured voxel aggregation with frequency-domain analysis for robust and high-precision brain tumor segmentation.