Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition.
Sensors (Basel)
; 23(4)2023 Feb 19.
Article
in En
| MEDLINE
| ID: mdl-36850906
ABSTRACT
This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Deep Learning
/
Neoplasms
Type of study:
Diagnostic_studies
Language:
En
Journal:
Sensors (Basel)
Year:
2023
Document type:
Article