Browsing by Author "Dogan, Serkan"

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Citation - WoS: 1
Motion Artifact Detection in Colonoscopy Images
(Sciendo, 2018) Kacmaz, Rukiye Nur; Yilmaz, Bulent; Dundar, Mehmet Sait; Dogan, Serkan
Computer-aided detection is an integral part of medical image evaluation process because examination of each image takes a long time and generally experts' do not have enough time for the elimination of images with motion artifact (blurred images). Computer-aided detection is required for both increasing accuracy rate and saving experts' time. Large intestine does not have straight structure thus camera of the colonoscopy should be moved continuously to examine inside of the large intestine and this movement causes motion artifact on colonoscopy images. In this study, images were selected from open-source colonoscopy videos and obtained at Kayseri Training and Research Hospital. Totally 100 images were analyzed half of which were clear. Firstly, a modified version of histogram equalization was applied in the pre-processing step to all images in our dataset, and then, used Laplacian, wavelet transform (WT), and discrete cosine transform-based (DCT) approaches to extract features for the discrimination of images with no artifact (clear) and images with motion artifact. The Laplacian-based feature extraction method was used for the first time in the literature on colonoscopy images. The comparison between Laplacian-based features and previously used methods such as WT and DCT has been performed. In the classification phase of our study, support vector machines (SVM), linear discriminant analysis (LDA), and k nearest neighbors (k-NN) were used as the classifiers. The results showed that Laplacian-based features were more successful in the detection of images with motion artifact when compared to popular methods used in the literature. As a result, a combination of features extracted using already existing approaches (WT and DCT) and the Laplacian-based methods reached 85% accuracy levels with SVM classification approach.
Vim-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression
(Nature Portfolio, 2025) Dogan, Refika Sultan; Akay, Ebru; Dogan, Serkan; Yilmaz, Bulent
The dataset in this study includes 202 videos with a total of 422 minutes, reaching Kayseri City Hospital's gastroenterology department as colonoscopy videos and 1903 microscopy images between 2019 and 2021. It includes 399 colonoscopy, microscopy images, and pathological diagnoses of polyps, as well as immunohistochemical staining results for proteins that play an important role in the assessment of cancerous cells, such as staining results for p53 (clone: bp53-11), Ki-67 (clone: 30-9), CD34 (clone: QBend/10), PD-L1 (clone: SP142), BRAF (clone: V600E) and VEGF (clone: SP125). By sharing the data openly, we aim to facilitate benchmarking, exploratory analysis and transfer-learning studies on colorectal polyps and cancer. In combination with external datasets or pretrained models, the resource can help advance data-driven detection and characterisation work. The diverse range of polyps assigned to cancer stages from 201 patients makes this tool valuable for researchers and clinicians in furthering diagnosis and treatment.
Citation - WoS: 20
Citation - Scopus: 26
Improved Classification of Colorectal Polyps on Histopathological Images With Ensemble Learning and Stain Normalization
(Elsevier Ireland Ltd, 2023) Yengec-Tasdemir, Sena Busra; Aydin, Zafer; Akay, Ebru; Dogan, Serkan; Yilmaz, Bulent
Background and Objective: Early detection of colon adenomatous polyps is critically important because correct detection of it significantly reduces the potential of developing colon cancers in the future. The key challenge in the detection of adenomatous polyps is differentiating it from its visually similar counterpart, non-adenomatous tissues. Currently, it solely depends on the experience of the pathologist. To assist the pathologists, the objective of this work is to provide a novel non-knowledge-based Clinical Decision Support System (CDSS) for improved detection of adenomatous polyps on colon histopathology images. Methods: The domain shift problem arises when the train and test data are coming from different distributions of diverse settings and unequal color levels. This problem, which can be tackled by stain normalization techniques, restricts the machine learning models to attain higher classification accuracies. In this work, the proposed method integrates stain normalization techniques with ensemble of competitively accurate, scalable and robust variants of CNNs, ConvNexts. The improvement is empirically analyzed for five widely employed stain normalization techniques. The classification performance of the proposed method is evaluated on three datasets comprising more than 10k colon histopathology images. Results: The comprehensive experiments demonstrate that the proposed method outperforms the stateof-the-art deep convolutional neural network based models by attaining 95% classification accuracy on the curated dataset, and 91.1% and 90% on EBHI and UniToPatho public datasets, respectively. Conclusions: These results show that the proposed method can accurately classify colon adenomatous polyps on histopathology images. It retains remarkable performance scores even for different datasets coming from different distributions. This indicates that the model has a notable generalization ability. (c) 2023 Elsevier B.V. All rights reserved.