Ultrasound imaging technologies for breast cancer detection and management: a review

R Guo, G Lu, B Qin, B Fei - Ultrasound in medicine & biology, 2018 - Elsevier
Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis.
In this review, we summarize ultrasound imaging technologies and their clinical applications …

Texture analysis of imaging: what radiologists need to know

BA Varghese, SY Cen, DH Hwang… - American Journal of …, 2019 - Am Roentgen Ray Soc
OBJECTIVE. Radiologic texture is the variation in image intensities within an image and is
an important part of radiomics. The objective of this article is to discuss some parameters …

Coronavirus disease (COVID-19) detection in chest X-ray images using majority voting based classifier ensemble

TB Chandra, K Verma, BK Singh, D Jain… - Expert systems with …, 2021 - Elsevier
Abstract Novel coronavirus disease (nCOVID-19) is the most challenging problem for the
world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS …

Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans

JZ Cheng, D Ni, YH Chou, J Qin, CM Tiu, YC Chang… - Scientific reports, 2016 - nature.com
This paper performs a comprehensive study on the deep-learning-based computer-aided
diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by …

A deep learning framework for supporting the classification of breast lesions in ultrasound images

S Han, HK Kang, JY Jeong, MH Park… - Physics in Medicine …, 2017 - iopscience.iop.org
A deep learning framework for supporting the classification of breast lesions in ultrasound
images - IOPscience Skip to content IOP Science home Accessibility Help Search Journals …

Segmentation information with attention integration for classification of breast tumor in ultrasound image

Y Luo, Q Huang, X Li - Pattern Recognition, 2022 - Elsevier
Breast cancer is one of the most common forms of cancer among women worldwide. The
development of computer-aided diagnosis (CAD) technology based on ultrasound imaging …

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

S Leger, A Zwanenburg, K Pilz, F Lohaus, A Linge… - Scientific reports, 2017 - nature.com
Radiomics applies machine learning algorithms to quantitative imaging data to characterise
the tumour phenotype and predict clinical outcome. For the development of radiomics risk …

Machine learning in ultrasound computer‐aided diagnostic systems: a survey

Q Huang, F Zhang, X Li - BioMed research international, 2018 - Wiley Online Library
The ultrasound imaging is one of the most common schemes to detect diseases in the
clinical practice. There are many advantages of ultrasound imaging such as safety …

Gray-level invariant Haralick texture features

T Löfstedt, P Brynolfsson, T Asklund, T Nyholm… - PloS one, 2019 - journals.plos.org
Haralick texture features are common texture descriptors in image analysis. To compute the
Haralick features, the image gray-levels are reduced, a process called quantization. The …

Classification of tumor in one single ultrasound image via a novel multi-view learning strategy

Y Luo, Q Huang, L Liu - Pattern Recognition, 2023 - Elsevier
Computer-aided diagnosis (CAD) technology has been widely used in the early diagnosis of
breast cancer. Nowadays, most of the existing breast ultrasound classification methods need …