Accuracy analysis of deep learning methods in breast cancer classification: A structured review
Breast cancer is diagnosed using histopathological imaging. This task is extremely time-
consuming due to high image complexity and volume. However, it is important to facilitate …
consuming due to high image complexity and volume. However, it is important to facilitate …
Classification of breast cancer histopathological images using DenseNet and transfer learning
Breast cancer is one of the most common invading cancers in women. Analyzing breast
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …
GARL-Net: Graph based adaptive regularized learning deep network for breast cancer classification
Across the globe, women suffer from breast cancer fatal disease. It is arising surprisingly due
to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many …
to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many …
Deep learning-and expert knowledge-based feature extraction and performance evaluation in breast histopathology images
H Kode, BD Barkana - Cancers, 2023 - mdpi.com
Simple Summary Breast cancer is one of the leading causes of cancer death among women.
Developing machine learning-based diagnosis models receives great attention from …
Developing machine learning-based diagnosis models receives great attention from …
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
According to the Global Cancer Observatory, 2020, breast cancer is the most prevalent
cancer type in both genders (11.7%), while prostate cancer is the second most common …
cancer type in both genders (11.7%), while prostate cancer is the second most common …
A new partially-coupled recursive least squares algorithm for multivariate equation-error systems
P Ma - International Journal of Control, Automation and …, 2023 - Springer
This paper focuses on the parameter estimation problems for multivariate pseudo-linear
systems. Based on the parameters coupling characteristic of the system model, a new …
systems. Based on the parameters coupling characteristic of the system model, a new …
Adaptive magnification network for precise tumor analysis in histopathological images
The variable magnification levels in histopathology images make it difficult to accurately
categorize tumor regions in breast cancer histology. In this study, a novel architecture for …
categorize tumor regions in breast cancer histology. In this study, a novel architecture for …
Vision transformer and its variants for image classification in digital breast cancer histopathology: A comparative study
A Sriwastawa, JA Arul Jothi - Multimedia Tools and Applications, 2024 - Springer
Abstract Convolutional Neural Networks (CNNs) have been the most popular image
classification tool for a long time. Inspired by the greater success of the transformer structure …
classification tool for a long time. Inspired by the greater success of the transformer structure …
Novel SiameseAE network for industrial process slow feature extraction and soft sensing applications
Industrial process data are time series data with strong dynamics and nonlinearities and are
based on temporal slowness. For industrial soft sensor modeling, it is critical to extract the …
based on temporal slowness. For industrial soft sensor modeling, it is critical to extract the …
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading
According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men
worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate …
worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate …