ArticleThe Eurasian journal of medicine2024
Recognition of Colon Polyps (Tubular Adenoma, Villous Adenoma) and Normal Colon Epithelium Histomorphology with Transfer Learning.
Article in The Eurasian journal of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Ensemble-Based Early Detection of Malaria via Explainable ViT-CNN Feature Fusion and SHAP.Journal of imaging informatics in medicine · 2026Article
- Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis.Journal of imaging · 2025Article
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Authors and funding
4 authors.
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Abstract
backgroundThe use of artificial intelligence technology in medicine, which is remarkable with its increasing use in many areas recently, has allowed for rapid developments. This technology quantitatively solves many problems in medicine, such as increased workload, delayed diagnosis, and treatment processes. It has been seen in the literature that this technology, which has a wide range of applications in medicine, also has a place in medical pathology. The main purpose of this study is to provide a histomorphological classification of colon polyps in the medical pathology department with high accuracy in a short time. Besides accelerating the diagnosis and treatment process, it is desired to facilitate the workload of the pathology department.
methodsThis study is based on the recognition of colon preparation images that come to the pathology department by using the image recognition techniques of artificial intelligence. VGG19, DenseNet201, and EfcientNetB7 models, which are convolutional neural networks (CNN) models, were used. A model based on a concatenation ensemble of 3 CNN architectures was also used in this study.
resultsWithin the scope of the study, a total of 515 preparation images, including normal epithelium, tubular adenoma and villous adenomas of the colon, were procured as data and introduced to artificial intelligence, and the diagnoses of these preparations were estimated histomorphologically by artificial intelligence.
conclusionAs a result of the study, an accuracy rate of 94.17% was achieved.
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