An enhanced Predictive heterogeneous ensemble model for breast cancer prediction
S Nanglia, M Ahmad, FA Khan, NZ Jhanjhi - Biomedical Signal Processing …, 2022 - Elsevier
Breast Cancer is one of the most prevalent tumors after lung cancer and is common in both
women and men. This disease is mostly asymptomatic in the early stages thus detection is …
women and men. This disease is mostly asymptomatic in the early stages thus detection is …
Breast tumor classification using an ensemble machine learning method
Breast cancer is the most common cause of death for women worldwide. Thus, the ability of
artificial intelligence systems to detect possible breast cancer is very important. In this paper …
artificial intelligence systems to detect possible breast cancer is very important. In this paper …
Optimized stacking ensemble learning model for breast cancer detection and classification using machine learning
Breast cancer is the most frequently encountered medical hazard for women in their forties,
affecting one in every eight women. It is the greatest cause of death worldwide, and early …
affecting one in every eight women. It is the greatest cause of death worldwide, and early …
An integrative machine learning framework for classifying SEER breast cancer
P Manikandan, U Durga, C Ponnuraja - Scientific Reports, 2023 - nature.com
Breast cancer is the commonest type of cancer in women worldwide and the leading cause
of mortality for females. The aim of this research is to classify the alive and death status of …
of mortality for females. The aim of this research is to classify the alive and death status of …
Feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis
Breast cancer is one of the leading causes of death among women, more so than all other
cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the …
cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the …
[Retracted] Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches
MM Khan, T Tazin, M Zunaid Hussain… - Computational …, 2022 - Wiley Online Library
Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting
in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer …
in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer …
Enhancing breast cancer detection and classification using advanced multi-model features and ensemble machine learning techniques
Breast cancer (BC) is the most common cancer among women, making it essential to have
an accurate and dependable system for diagnosing benign or malignant tumors. It is …
an accurate and dependable system for diagnosing benign or malignant tumors. It is …
An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi …
S Chatterjee, A Das - Soft Computing, 2023 - Springer
Breast cancer is a life-threatening and consequential disease due to its invasive and
proliferative trait, predominantly found in women. Early detection of the cancer is a …
proliferative trait, predominantly found in women. Early detection of the cancer is a …
Square static–deep hyper optimization and genetic meta-learning approach for disease classification
A recent study showed that machine learning algorithms are trained to diagnose the disease
in accurate manner. The stroke prediction of people based on various features is identified …
in accurate manner. The stroke prediction of people based on various features is identified …
Breast cancer classification using snapshot ensemble deep learning model and t-distributed stochastic neighbor embedding
The current work aims to analyse the historical data pertaining to breast cancer to detect and
predict the disease. For the same, authors employ t-distributed stochastic neighbor …
predict the disease. For the same, authors employ t-distributed stochastic neighbor …