A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images

Autor: Sang Hee Jo, Yoonhee Kim, Yoon Bum Lee, Sung Suk Oh, Jong-ryul Choi
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Innovative Optical Health Sciences, Vol 14, Iss 6, Pp 2150018-1-2150018-9 (2021)
Druh dokumentu: article
ISSN: 1793-5458
1793-7205
17935458
DOI: 10.1142/S1793545821500188
Popis: 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 accuracies 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 classification models and performance comparisons, we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.
Databáze: Directory of Open Access Journals
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