Recently,research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms.In this study,we established machine learning-based algorithms to detect phot...Recently,research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms.In this study,we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains.Six machine learning-based algorithms for binary classification were applied,and the accu-racies were compared to classify normal tissues and photothrombotic lesions.The lesion classification model consisting of a 3-layered neural network with a rectified linear unit(ReLU)activation function,Xavier initialization,and Adam optimization using datasets with a unit size of 128×128 pixels yielded the highest accuracy(0.975).In the validation using the tested histological images,it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke.Through the development of machine learning-based photothrombotic lesion classi-fication models and performance comparisons,we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.展开更多
基金This research was supported by grants from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare(Hl17C1501)from Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science&ICT(NRF-2020R1C1C1012230)S.H,Cho was supported by the semester internship program between Daegu Catholic University and Daegu-Gyeongbuk Medical Innovation Foundation.
文摘Recently,research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms.In this study,we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains.Six machine learning-based algorithms for binary classification were applied,and the accu-racies were compared to classify normal tissues and photothrombotic lesions.The lesion classification model consisting of a 3-layered neural network with a rectified linear unit(ReLU)activation function,Xavier initialization,and Adam optimization using datasets with a unit size of 128×128 pixels yielded the highest accuracy(0.975).In the validation using the tested histological images,it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke.Through the development of machine learning-based photothrombotic lesion classi-fication models and performance comparisons,we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.
