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- BildiriYalnız künye2023Deep Learning and Transfer Learning Based Brain Tumor Segmentation; [Derin Ogrenme ve Transfer Ogrenme Tabanli Beyin Tumoru Segmentasyonu]
Eker, Ayse Gul; Pehlivanoglu, Meltem Kurt; Ince, Ibrahim; Duru, Nevcihan
In brain tumors, which are formed by the abnormal growth of cells in brain tissue, early diagnosis, and accurate treatment are crucial. Brain tumor segmentation provides experts with the ability to accurately identify brain tumors and determine their location. Through segmentation, the type, size, and shape of tumors can be determined, enabling the application of appropriate treatment. In this study, the aim was to perform brain tumor segmentation on the Brain Tumor Figshare (BTF) dataset, which includes diverse images in terms of structural complexity, viewing angles, different device usage, noise, and bias field effects, without applying any preprocessing. Basic models such as U-Net and FCN were utilized, along with transfer learning-based approaches using VGG, XceptionNet, InceptionNet, and ResNet architectures as the underlying structures in these models. According to the experimental results, the 16-layer FCN architecture with VGG- 19 as the underlying structure achieved the highest score with a dice score of 0.9169.
AtıfScopus12
İncele - BildiriYalnız künye2023Brain Tumor Classification Using Deep Learning Algorithms; [Derin Ogrenme Algoritmalan ile Beyin Tiimoru Siniflandirma]
Pehlivanoglu, Meltem Kurt; Eker, Ayse Gul; Ince, Ibrahim; Karhan, Deniz; Duru, Nevcihan
Brain tumor classification categorizes brain tumors based on their various characteristics. It aids in determining suitable treatment strategies and predicting patient outcomes. Therefore, the objective of this study is to identify the optimal tumor classification model by utilizing various Convolutional Neural Network (CNN)-based deep learning models (including combined models) on a recently published tumor classification dataset. Furthermore, diverse image manipulation and augmentation techniques were applied and their impact on model performance was evaluated. The experimental results prove that the combined deep learning models outperform other approaches in terms of performance in multi-class brain tumor classification.
AtıfScopus3İncele - BildiriYalnız künye2023A Comparison of Grammatical Error Correction Models in English Writing; [Ingilizce Yaziminda Dil Bilgisel Hata Duzeltme Modellerinin Karsilastinlmasi]
Eker, Kadir; Pehlivanoglu, Meltem Kurt; Eker, Ayse Gul; Syakura, Muhammad Abdan; Duru, Nevcihan
Grammatical error correction (GEC) is the task of correcting grammatical, spelling, and word usage errors in texts. Detecting and correcting these errors makes it difficult for the reader to understand the text and negatively affects communication. Editors and proofreaders can identify and correct grammatical errors while reading texts. However, this manual correction takes a long time. With the development of technology, artificial intelligence-supported tools have been developed that automatically correct errors. In artificial intelligence-supported systems, this task is usually performed using natural language processing methods. In this study, a traditional neural network architecture (Attention BiLSTM) and a transformer-based language model (T5) were trained. Then, the success of these trained models and Large Language Models (LLMs) (ChatGPT, Google Bard, LanguageTool) in English grammatical error correction has been compared on the JFLEG dataset. According to experimental results, the pre-trained T5 language model beats others in all evaluation metrics for grammatical error correction.
AtıfScopus6İncele