Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
Breast cancer is a common and fatal disease among women worldwide. Therefore, the early
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
A comprehensive review for breast histopathology image analysis using classical and deep neural networks
Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …
histopathological images contain sufficient phenotypic information, they play an …
An inception‐ResNet deep learning approach to classify tumours in the ovary as benign and malignant
A Kodipalli, S Guha, S Dasar, T Ismail - Expert Systems, 2022 - Wiley Online Library
The classification of tumours into benign and malignant continues to date to be a very
relevant and significant research topic in the cancer research domain. With the advent of …
relevant and significant research topic in the cancer research domain. With the advent of …
A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images
The tumor grading of laryngeal cancer pathological images needs to be accurate and
interpretable. The deep learning model based on the attention mechanism-integrated …
interpretable. The deep learning model based on the attention mechanism-integrated …
Transfer learning-assisted multi-resolution breast cancer histopathological images classification
Breast cancer is one of the leading death cause among women nowadays. Several methods
have been proposed for the detection of breast cancer. Various machine learning-based …
have been proposed for the detection of breast cancer. Various machine learning-based …
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 …
Application of transfer learning and ensemble learning in image-level classification for breast histopathology
Background Breast cancer has the highest prevalence among all cancers in women
globally. The classification of histopathological images in the diagnosis of breast cancers is …
globally. The classification of histopathological images in the diagnosis of breast cancers is …
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 …
Conventional machine learning versus deep learning for magnification dependent histopathological breast cancer image classification: A comparative study with …
Breast cancer is a serious threat to women. Many machine learning-based computer-aided
diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer …
diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer …
Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided …
Tumor grading and interpretability of laryngeal cancer is a key yet challenging task in the
clinical diagnosis, mainly because of the commonly used low-magnification pathological …
clinical diagnosis, mainly because of the commonly used low-magnification pathological …