WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 15Citation - Scopus: 18Histopathology Image Classification: Highlighting the Gap Between Manual Analysis and AI Automation(Frontiers Media S.A., 2024-01-17) Dogan, Refika Sultan; Yilmaz, BulentThe 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.Article Citation - WoS: 9Citation - Scopus: 10An Answer to Colon Cancer Treatment by Mesenchymal Stem Cell Originated from Adipose Tissue(Mashhad Univ Med Sciences, 2018) Iplik, Elif Sinem; Ertugrul, Baris; Kozanoglu, Ilknur; Baran, Yusuf; Cakmakoglu, BediaObjective(s): Colon cancer is risen up with its complex mechanism that directly impacts on its treatment as well as its common prevalence. Mesenchymal stem cells (MSCs) have been considered as a therapeutic candidate for conventional disease including cancer. In this research, we have focused on apoptotic effects of adipose tissue-derived MSCs in colon cancer. Materials and Methods: MSCs were obtained from adipose tissue and characterized by Flowcytometer using suitable antibodies. MSCs, HT-29, HCT-116, RKO and healthy cell line MRC5 were cultured by different seeding procedure. After cell viability assay, changes in caspase 3 enzyme activity and the level of phosphatidylserine were measured. Results: For cell viability assay, a 48 hr incubation period was chosen to seed all cells together. There was a 1.36-fold decrease in caspase 3 enzyme activity by co-treatment of RKO and MSCs in addition to 2.02-fold decrease in HT-29 and MSCs co-treatment, and 1.103-fold increase in HCT-116 and MSCs. The results demonstrated that HCT-116 led to the highest rate of apoptotic cell death (7.5%) compared with other cells. Conclusion: We suggest that MSCs might remain a new treatment option for cancer by its differentiation and repair capacity.