Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 5Citation - Scopus: 7Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors(IEEE-Inst Electrical Electronics Engineers Inc, 2023-10) Altindis, Fatih; Banerjee, Antara; Phlypo, Ronald; Yilmaz, Bulent; Congedo, MarcoThis article presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is evaluated on brain-computer interface (BCI) data and optimal hyper-parameter values of the algorithm are studied for classification performance and computational cost. Six publicly available P300 databases comprising 333 sessions from 177 subjects are used. As compared to the conventional subject-specific train/test pipeline, both group learning and fast alignment significantly improve the classification accuracy except for the database with clinical subjects (average improvement: 2.12 +/- 1.88%). GALIA utilizes cyclic approximate joint diagonalization (AJD) to find a set of linear transformations, one for each domain, jointly aligning the feature vectors of all domains. Group learning achieves a many-to-many transfer learning without compromising the classification performance on non-clinical BCI data. Fast alignment further extends the group learning for any unseen domains, allowing a many-to-one transfer learning with the same properties. The former method creates a single machine learning model using data from previous subjects and/or sessions, whereas the latter exploits the trained model for an unseen domain requiring no further training of the classifier.Article Citation - WoS: 28Citation - Scopus: 31Super Resolution Convolutional Neural Network Based Pre-Processing for Automatic Polyp Detection in Colonoscopy Images(Pergamon-Elsevier Science Ltd, 2021-03) Tas, Merve; Yilmaz, BulentColonoscopy is the most common methodology used to detect polyps on the colon surface. Increasing the image resolution has the potential to improve the automatic colonoscopy based diagnosis and polyp detection and localization. In this study, we proposed a pre-processing approach that uses convolutional neural network based super resolution method (SRCNN) to increase the resolution of the training colonoscopy images before the localization of polyps. We also investigated the use of CNN based models such as the Single Shot MultiBox Detector (SSD) and Faster Regional CNN (RCNN) for real-time polyp detection and localization. Our results showed that using SRCNN method before the training process provides better results in terms of accuracy in both models compared to the low-resolution cases. Furthermore, we reached an F2 score of 0.945 for the correct localization of colon polyps using Faster RCNN with ResNet-101 feature extractor.Conference Object Citation - Scopus: 4Re-Exploring the Kayseri Culture Route by Using Deep Learning for Cultural Heritage Image Classification Cultural Heritage Image Classification by Using Deep Learning: Kayseri Culture Route(Association for Computing Machinery, 2024-05-25) Kevseroğlu, Ozlem; Kurban, RifatThe categorization of images captured during the documentation of architectural structures is a crucial aspect of preserving cultural heritage in digital form. Dealing with a large volume of images makes this categorization process laborious and time-consuming, often leading to errors. Introducing automatic techniques to aid in sorting would streamline this process, enhancing the efficiency of digital documentation. Proper classification of these images facilitates improved organization and more effective searches using specific terms, thereby aiding in the analysis and interpretation of the heritage asset. This study primarily focuses on applying deep learning techniques, specifically SqueezeNet convolutional neural networks (CNNs), for classifying images of architectural heritage. The effectiveness of training these networks from scratch versus fine-tuning pre-existing models is examined. In this study, we concentrate on identifying significant elements within images of buildings with architectural heritage significance of Kayseri Culture Route. Since no suitable datasets for network training were found, a new dataset was created. Transfer learning enables the use of pre-trained convolutional neural networks to specific image classification tasks. In the experiments, 99.8% of classification accuracy have been achieved by using SqueezeNet, suggesting that the implementation of the technique can substantially enhance the digital documentation of architectural heritage. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 11Citation - Scopus: 14Deep Learning Based Semantic Segmentation and Quantification for MRD Biochip Images(Elsevier Sci Ltd, 2022-08) Celebi, Fatma; Tasdemir, Kasim; Icoz, KutayMicrofluidic platforms offer prominent advantages for the early detection of cancer and monitoring the patient response to therapy. Numerous microfluidic platforms have been developed for capturing and quantifying the tumor cells integrating several readout methods. Earlier, we have developed a microfluidic platform (MRD Biochip) to capture and quantify leukemia cells. This is the first study which employs a deep learning-based segmentation to the MRD Biochip images consisting of leukemic cells, immunomagnetic beads and micropads. Implementing deep learning algorithms has two main contributions; firstly, the quantification performance of the readout method is improved for the unbalanced dataset. Secondly, unlike the previous classical computer vision -based method, it does not require any manual tuning of the parameters which resulted in a more generalized model against variations of objects in the image in terms of size, color, and noise. As a result of these benefits, the proposed system is promising for providing real time analysis for microfluidic systems. Moreover, we compare different deep learning based semantic segmentation algorithms on the image dataset which are acquired from the real patient samples using a bright-field microscopy. Without cell staining, hyper-parameter optimized, and modified U-Net semantic segmentation algorithm yields 98.7% global accuracy, 86.1% mean IoU, 92.2% mean precision, 92.2% mean recall and 92.2% mean F-1 score measure on the patient dataset. After segmentation, quantification result yields 89% average precision, 97% average recall on test images. By applying the deep learning algorithms, we are able to improve our previous results that employed conventional computer vision methods.Article Citation - Scopus: 14CoviDetector: A Transfer Learning-Based Semi Supervised Approach to Detect COVID-19 Using CXR Images(Elsevier B.V., 2023-06) Chowdhury, Deepraj; Das, Anik; Dey, Ajoy; Banerjee, Soham; Golec, Muhammed; Kollias, Dimitrios; Arya, Rajesh Chand; Uhlig, SteveCOVID-19 was one of the deadliest and most infectious illnesses of this century. Research has been done to decrease pandemic deaths and slow down its spread. COVID-19 detection investigations have utilised Chest X-ray (CXR) images with deep learning techniques with its sensitivity in identifying pneumonic alterations. However, CXR images are not publicly available due to users’ privacy concerns, resulting in a challenge to train a highly accurate deep learning model from scratch. Therefore, we proposed CoviDetector, a new semi-supervised approach based on transfer learning and clustering, which displays improved performance and requires less training data. CXR images are given as input to this model, and individuals are categorised into three classes: (1) COVID-19 positive; (2) Viral pneumonia; and (3) Normal. The performance of CoviDetector has been evaluated on four different datasets, achieving over 99% accuracy on them. Additionally, we generate heatmaps utilising Grad-CAM and overlay them on the CXR images to present the highlighted areas that were deciding factors in detecting COVID-19. Finally, we developed an Android app to offer a user-friendly interface. We release the code, datasets and results’ scripts of CoviDetector for reproducibility purposes; they are available at: https://github.com/dasanik2001/CoviDetector © 2024 Elsevier B.V., All rights reserved.Article Citation - Scopus: 15An Effective Colorectal Polyp Classification for Histopathological Images Based on Supervised Contrastive Learning(Elsevier Ltd, 2024-04) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Doǧan, Serkan; Yilmaz, BulentEarly detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure precise classification of colon histopathology images. Significantly, we have integrated the Big Transfer model, which has gained prominence for its exemplary adaptability to visual tasks in medical imaging. Our novel approach discerns between in-class and out-of-class images, thereby elevating its discriminatory power for polyp subtypes. We validated our system using two datasets: a specially curated one and the publicly accessible UniToPatho dataset. The results reveal that our model markedly surpasses traditional deep convolutional neural networks, registering classification accuracies of 87.1% and 70.3% for the custom and UniToPatho datasets, respectively. Such results emphasize the transformative potential of our model in polyp classification endeavors. © 2024 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 4A Transfer Learning Application on the Reliability of Psychological Drugs' Comments(Institute of Electrical and Electronics Engineers Inc., 2023-07-25) Sen, Tarik Uveys; Bakal, GokhanAs digitalization and the Internet stay emerging concepts by gaining popularity, the accuracy of personal reviews/opinions will be a critical issue. This circumstance also particularly applies to patients taking psychological drugs, where accurate information is crucial for other patients and medical professionals. In this study, we analyze drug reviews from drugs.com to determine the effectiveness of reviews for psychological drugs. Our dataset includes over 200,000 drug reviews, which we labeled as positive, negative, or neutral according to their rating scores. We apply machine learning (ML) models, including Logistic Regression, Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) algorithms, to predict the sentiment class of each review. Our results demonstrate an F1-Weighted score of 85.3% for the LSTM model. However, by applying the transfer learning technique, we further improved the F1 score (nearly 3% increase) obtained by the LSTM model. Our findings proved that there is no contextual difference between the comments made by the patients suffering from psychological or other diseases. © 2023 Elsevier B.V., All rights reserved.Article Citation - WoS: 2Citation - Scopus: 3A Comprehensive Study on Automatic Non-Informative Frame Detection in Colonoscopy Videos(Wiley, 2024-01) Kacmaz, Rukiye Nur; Dogan, Refika Sultan; Yilmaz, BuelentDespite today's developing healthcare technology, conventional colonoscopy is still a gold-standard method to detect colon abnormalities. Due to the folded structure of the intestine and visual disturbances caused by artifacts, it can be hard for specialists to detect abnormalities during the procedure. Frames that include artifacts such as specular reflection, improper contrast levels from insufficient or excessive illumination gastric juice, bubbles, or residuals should be detected to increase an accurate diagnosis rate. In this work, both conventional machine learning and transfer learning methods have been used to detect non-informative frames in colonoscopy videos. The conventional machine learning part consists of 5 different types of texture features, which are gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray-tone difference matrix (NGTDM), focus measure operators (FMOs), and first-order statistics. In addition to these methods, we utilized 8 different transfer learning models: AlexNet, SqueezeNet, GoogleNet, ShuffleNet, ResNet50, ResNet18, NasNetMobile, and MobileNet. The results showed that FMOs and decision tree combination gave the best accuracy and f-measure values with almost 89% and 0.79%, respectively, for the conventional machine learning part. When the transfer learning part is taken into account, AlexNet (99.85%) and SqueezeNet (98.80%) have the highest performance metric results. This study shows the potential of both transfer learning and conventional machine learning algorithms to provide fast and accurate non-informative frame detection to be used during a colonoscopy, which may be considered the initial step in identifying and classifying colon-related diseases automatically to help guide physicians.