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

Autor:	Yoon Bum Lee, Sang Hee Jo, Yoonhee Kim, Jong-ryul Choi, Sung Suk Oh
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Technology multi-layer neural networks Computer science Biomedical Engineering Medicine (miscellaneous) Logistic regression Machine learning computer.software_genre Lesion medicine binary classification business.industry logistic regression QC350-467 Rabbit brain Optics. Light histopathological images Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials photothrombotic lesion rabbit brain machine learning Binary classification Artificial intelligence medicine.symptom business computer
Zdroj:	Journal of Innovative Optical Health Sciences, Vol 14, Iss 6, Pp 2150018-1-2150018-9 (2021)
ISSN:	1793-7205 1793-5458
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 [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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bfa0d1a6927a4834d8e0d9d900a3cc7 http://www.worldscientific.com/doi/epdf/10.1142/S1793545821500188 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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