Cargando…

Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network

It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a conv...

Descripción completa

Detalles Bibliográficos
Autores principales: Hizukuri, Akiyoshi, Nakayama, Ryohei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163984/
https://www.ncbi.nlm.nih.gov/pubmed/30044441
http://dx.doi.org/10.3390/diagnostics8030048
_version_ 1783359492213178368
author Hizukuri, Akiyoshi
Nakayama, Ryohei
author_facet Hizukuri, Akiyoshi
Nakayama, Ryohei
author_sort Hizukuri, Akiyoshi
collection PubMed
description It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our database consisted of 578 breast ultrasonographic images. It included 287 malignant (217 invasive carcinomas and 70 noninvasive carcinomas) and 291 benign lesions (111 cysts and 180 fibroadenomas). In this study, the CNN constructed from four convolutional layers, three batch-normalization layers, four pooling layers, and two fully connected layers was employed for distinguishing between the four different types of histological classifications for lesions. The classification accuracies for histological classifications with our CNN model were 83.9–87.6%, which were substantially higher than those with our previous method (55.7–79.3%) using hand-crafted features and a classifier. The area under the curve with our CNN model was 0.976, whereas that with our previous method was 0.939 (p = 0.0001). Our CNN model would be useful in differential diagnoses of breast lesions as a diagnostic aid.
format Online
Article
Text
id pubmed-6163984
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-61639842018-10-11 Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network Hizukuri, Akiyoshi Nakayama, Ryohei Diagnostics (Basel) Article It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our database consisted of 578 breast ultrasonographic images. It included 287 malignant (217 invasive carcinomas and 70 noninvasive carcinomas) and 291 benign lesions (111 cysts and 180 fibroadenomas). In this study, the CNN constructed from four convolutional layers, three batch-normalization layers, four pooling layers, and two fully connected layers was employed for distinguishing between the four different types of histological classifications for lesions. The classification accuracies for histological classifications with our CNN model were 83.9–87.6%, which were substantially higher than those with our previous method (55.7–79.3%) using hand-crafted features and a classifier. The area under the curve with our CNN model was 0.976, whereas that with our previous method was 0.939 (p = 0.0001). Our CNN model would be useful in differential diagnoses of breast lesions as a diagnostic aid. MDPI 2018-07-25 /pmc/articles/PMC6163984/ /pubmed/30044441 http://dx.doi.org/10.3390/diagnostics8030048 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hizukuri, Akiyoshi
Nakayama, Ryohei
Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
title Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
title_full Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
title_fullStr Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
title_full_unstemmed Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
title_short Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network
title_sort computer-aided diagnosis scheme for determining histological classification of breast lesions on ultrasonographic images using convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163984/
https://www.ncbi.nlm.nih.gov/pubmed/30044441
http://dx.doi.org/10.3390/diagnostics8030048
work_keys_str_mv AT hizukuriakiyoshi computeraideddiagnosisschemefordetermininghistologicalclassificationofbreastlesionsonultrasonographicimagesusingconvolutionalneuralnetwork
AT nakayamaryohei computeraideddiagnosisschemefordetermininghistologicalclassificationofbreastlesionsonultrasonographicimagesusingconvolutionalneuralnetwork