Accepted for/Published in: JMIR Biomedical Engineering
Date Submitted: Aug 24, 2020
Open Peer Review Period: Aug 24, 2020 - Nov 9, 2020
Date Accepted: Apr 4, 2021
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Neural network pattern recognition of ultrasound image gray scale intensity histogram of breast lesions to differentiate between benign and malignant lesions
ABSTRACT
The aim of this study is to analyze the effectiveness of grayscale intensity histogram to differentiate benign and malignant lesions using a convolutional neural network. Data (200 USG images, 100-malignant, 100-benign) was downloaded from an online access repository. The images were despeckled using ImageJ software and the grayscale intensity histogram values were extracted. In-built neural network pattern recognition application in Matlab R2019b was used to classify the images, which is a two-layer feed-forward network, with sigmoid hidden and softmax output neurons. The positive predictive value of the CNN was 95%. The best performance of 0.078264 was achieved at 36 epochs in the validation set. This study suggests that the grayscale intensity histogram of a USG image is an easy and feasible method to identify malignant lesions through an artificial neural network.
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