Browsing by Author "Doğan, Refika Sultan"

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A comprehensive study on automatic non-informative frame detection in colonoscopy videos
(WILEY, 2024) Kaçmaz, Rukiye Nur; Doğan, Refika Sultan; Yılmaz, Bülent; 0000-0001-8416-1765; 0000-0003-2954-1217; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Biyomühendislik Bölümü; Doğan, Refika Sultan; Yılmaz, Bülent
Despite 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.
Histopathology image classification: highlighting the gap between manual analysis and AI automation
(FRONTIERS MEDIA SA, 2023) Doğan, Refika Sultan; Yılmaz, Bülent; 0000-0001-8416-1765; 0000-0003-2954-1217; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Biyomühendislik Bölümü; Doğan, Refika Sultan; Yılmaz, Bülent
The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.