Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …
mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC …
Deep learning for medical image-based cancer diagnosis
Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …
diagnosis. To help readers better understand the current research status and ideas, this …
Classification of breast tumors based on histopathology images using deep features and ensemble of gradient boosting methods
MR Abbasniya, SA Sheikholeslamzadeh… - Computers and …, 2022 - Elsevier
Breast cancer is the most common cancer among women worldwide. Early-stage diagnosis
of this disease can significantly improve the efficiency of treatment. Computer-Aided …
of this disease can significantly improve the efficiency of treatment. Computer-Aided …
Breast cancer detection in thermograms using a hybrid of GA and GWO based deep feature selection method
Breast cancer is one of the most common reasons for the premature death of women
worldwide. However, early detection and diagnosis of the same can save many lives …
worldwide. However, early detection and diagnosis of the same can save many lives …
Binary aquila optimizer for selecting effective features from medical data: A COVID-19 case study
MH Nadimi-Shahraki, S Taghian, S Mirjalili… - Mathematics, 2022 - mdpi.com
Medical technological advancements have led to the creation of various large datasets with
numerous attributes. The presence of redundant and irrelevant features in datasets …
numerous attributes. The presence of redundant and irrelevant features in datasets …
Binary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical data
Many metaheuristic approaches have been developed to select effective features from
different medical datasets in a feasible time. However, most of them cannot scale well to …
different medical datasets in a feasible time. However, most of them cannot scale well to …
Traditional machine learning algorithms for breast cancer image classification with optimized deep features
For breast cancer diagnosis, computer-aided classification of histopathological images is of
critical importance for correct and early diagnosis. Transfer learning approaches for feature …
critical importance for correct and early diagnosis. Transfer learning approaches for feature …
Migration-based moth-flame optimization algorithm
Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that
demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO …
demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO …
Breast cancer diagnosis using optimized deep convolutional neural network based on transfer learning technique and improved Coati optimization algorithm
MM Emam, EH Houssein, NA Samee… - Expert Systems with …, 2024 - Elsevier
Breast cancer is a significant health concern due to its aggressive nature and high mortality
rates. Early detection is crucial to improving patient outcomes. Thermography, a non …
rates. Early detection is crucial to improving patient outcomes. Thermography, a non …
HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction
Abstract Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of
most human cells and exerts an essential role in the immune system responding to the …
most human cells and exerts an essential role in the immune system responding to the …