Open AccessA comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images
Author(s) -
Sang Hee Jo,
Yoonhee Kim,
Yoon Bum Lee,
Se Baek Oh,
Jong-ryul Choi
Publication year - 2021
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545821500188
Subject(s) - artificial intelligence , lesion , initialization , histopathology , pattern recognition (psychology) , binary classification , artificial neural network , computer science , pathology , support vector machine , machine learning , medicine , programming language
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 [Formula: see text] 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.