Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
EH Houssein, MM Emam, AA Ali… - Expert Systems with …, 2021 - Elsevier
Breast cancer is the second leading cause of death for women, so accurate early detection
can help decrease breast cancer mortality rates. Computer-aided detection allows …
can help decrease breast cancer mortality rates. Computer-aided detection allows …
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 learning for image-based cancer detection and diagnosis− A survey
In this paper, we aim to provide a survey on the applications of deep learning for cancer
detection and diagnosis and hope to provide an overview of the progress in this field. In the …
detection and diagnosis and hope to provide an overview of the progress in this field. In the …
Deep learning based methods for breast cancer diagnosis: a systematic review and future direction
Breast cancer is one of the precarious conditions that affect women, and a substantive cure
has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep …
has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep …
Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images
Automated nuclear detection is a critical step for a number of computer assisted pathology
related image analysis algorithms such as for automated grading of breast cancer tissue …
related image analysis algorithms such as for automated grading of breast cancer tissue …
A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images
J Xu, X Luo, G Wang, H Gilmore, A Madabhushi - Neurocomputing, 2016 - Elsevier
Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated
segmentation or classification of EP and ST tissues is important when developing …
segmentation or classification of EP and ST tissues is important when developing …
Deep learning in mammography and breast histology, an overview and future trends
A Hamidinekoo, E Denton, A Rampun, K Honnor… - Medical image …, 2018 - Elsevier
Recent improvements in biomedical image analysis using deep learning based neural
networks could be exploited to enhance the performance of Computer Aided Diagnosis …
networks could be exploited to enhance the performance of Computer Aided Diagnosis …
Gearbox fault diagnosis using a deep learning model with limited data sample
Massive volumes of data are needed for deep learning (DL) models to provide accurate
diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the …
diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the …
BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights
There are several breast cancer datasets for building Computer Aided Diagnosis systems
(CADs) using either deep learning or traditional models. However, most of these datasets …
(CADs) using either deep learning or traditional models. However, most of these datasets …
Deep learning applied for histological diagnosis of breast cancer
Y Yari, TV Nguyen, HT Nguyen - IEEE Access, 2020 - ieeexplore.ieee.org
Deep learning, as one of the currently most popular computer science research trends,
improves neural networks, which has more and deeper layers allowing higher abstraction …
improves neural networks, which has more and deeper layers allowing higher abstraction …