Neural Network Pattern Recognition of Ultrasound Image Gray Scale Intensity Histograms of Breast Lesions to Differentiate Between Benign and Malignant Lesions: Analytical Study.

Autor:	Ramachandran A; Postgraduate Institute of Medical Education and Research, Chandigarh, India., Kathavarayan Ramu S; Mahatma Gandhi Medical College and Research Institute, Puducherry, India.; All India Institute of Medical Sciences, New Delhi, India.
Jazyk:	angličtina
Zdroj:	JMIR biomedical engineering [JMIR Biomed Eng] 2021 Jun 02; Vol. 6 (2), pp. e23808. Date of Electronic Publication: 2021 Jun 02.
DOI:	10.2196/23808
Abstrakt:	Background: Ultrasound-based radiomic features to differentiate between benign and malignant breast lesions with the help of machine learning is currently being researched. The mean echogenicity ratio has been used for the diagnosis of malignant breast lesions. However, gray scale intensity histogram values as a single radiomic feature for the detection of malignant breast lesions using machine learning algorithms have not been explored yet. Objective: This study aims to assess the utility of a simple convolutional neural network in classifying benign and malignant breast lesions using gray scale intensity values of the lesion. Methods: An open-access online data set of 200 ultrasonogram breast lesions were collected, and regions of interest were drawn over the lesions. The gray scale intensity values of the lesions were extracted. An input file containing the values and an output file consisting of the breast lesions' diagnoses were created. The convolutional neural network was trained using the files and tested on the whole data set. Results: The trained convolutional neural network had an accuracy of 94.5% and a precision of 94%. The sensitivity and specificity were 94.9% and 94.1%, respectively. Conclusions: Simple neural networks, which are cheap and easy to use, can be applied to diagnose malignant breast lesions with gray scale intensity values obtained from ultrasonogram images in low-resource settings with minimal personnel. (©Arivan Ramachandran, Shivabalan Kathavarayan Ramu. Originally published in JMIR Biomedical Engineering (http://biomsedeng.jmir.org), 02.06.2021.)
Databáze:	MEDLINE
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