A comprehensive review on breast cancer detection, classification and segmentation using deep learning
The incidence and mortality rate of Breast Cancer (BC) are global problems for women, with
over 2.1 million new diagnoses each year worldwide. There is no age range, race, or …
over 2.1 million new diagnoses each year worldwide. There is no age range, race, or …
Deep convolutional neural networks for mammography: advances, challenges and applications
D Abdelhafiz, C Yang, R Ammar, S Nabavi - BMC bioinformatics, 2019 - Springer
Background The limitations of traditional computer-aided detection (CAD) systems for
mammography, the extreme importance of early detection of breast cancer and the high …
mammography, the extreme importance of early detection of breast cancer and the high …
Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs
Purpose To compare the performance of a deep-learning bone age assessment model
based on hand radiographs with that of expert radiologists and that of existing automated …
based on hand radiographs with that of expert radiologists and that of existing automated …
Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis
J Zheng, D Lin, Z Gao, S Wang, M He, J Fan - IEEE Access, 2020 - ieeexplore.ieee.org
Breast cancer is one of the most dangerous diseases and the second largest cause of
female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow …
female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow …
Representation learning for mammography mass lesion classification with convolutional neural networks
Background and objective The automatic classification of breast imaging lesions is currently
an unsolved problem. This paper describes an innovative representation learning …
an unsolved problem. This paper describes an innovative representation learning …
Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring
Mammographic risk scoring has commonly been automated by extracting a set of
handcrafted features from mammograms, and relating the responses directly or indirectly to …
handcrafted features from mammograms, and relating the responses directly or indirectly to …
Discrimination of breast cancer with microcalcifications on mammography by deep learning
J Wang, X Yang, H Cai, W Tan, C Jin, L Li - Scientific reports, 2016 - nature.com
Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic
accuracy of microcalcifications, this study evaluates the performance of deep learning-based …
accuracy of microcalcifications, this study evaluates the performance of deep learning-based …
Fully convolutional architectures for multiclass segmentation in chest radiographs
AA Novikov, D Lenis, D Major… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
The success of deep convolutional neural networks (NNs) on image classification and
recognition tasks has led to new applications in very diversified contexts, including the field …
recognition tasks has led to new applications in very diversified contexts, including the field …
[HTML][HTML] The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review
This paper reviews objective methods for prognostic modelling of cancer tumours located
within radiology images, a process known as radiomics. Radiomics is a novel feature …
within radiology images, a process known as radiomics. Radiomics is a novel feature …
Automated analysis of unregistered multi-view mammograms with deep learning
G Carneiro, J Nascimento… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
We describe an automated methodology for the analysis of unregistered cranio-caudal (CC)
and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk …
and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk …