Applications and techniques of machine learning in cancer classification: A systematic review
The domain of Machine learning has experienced Substantial advancement and
development. Recently, showcasing a Broad spectrum of uses like Computational …
development. Recently, showcasing a Broad spectrum of uses like Computational …
Breast cancer classification using Deep Q Learning (DQL) and gorilla troops optimization (GTO)
Breast cancer (BC) is a primary reason for death among the female population around the
world. Early identification can aid in decreasing the mortality rates associated with this …
world. Early identification can aid in decreasing the mortality rates associated with this …
B2C3NetF2: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature …
Currently, the improvement in AI is mainly related to deep learning techniques that are
employed for the classification, identification, and quantification of patterns in clinical …
employed for the classification, identification, and quantification of patterns in clinical …
Combining ensemble classification and integrated filter-evolutionary search for breast cancer diagnosis
X Sun, A Qourbani - Journal of Cancer Research and Clinical Oncology, 2023 - Springer
Introduction Breast cancer is one of the most common chronic diseases and the second
cause of death among women, where its timely diagnosis plays an important role in survival …
cause of death among women, where its timely diagnosis plays an important role in survival …
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 …
Challenges to the Early Diagnosis of Breast Cancer: Current Scenario and the Challenges Ahead
Breast cancer is still a major problem for medical research, science, and society. Breast
cancer is the most common form of cancer among women and has a high rate of mortality …
cancer is the most common form of cancer among women and has a high rate of mortality …
Reinforcement learning (RL)-based semantic segmentation and attention based backpropagation convolutional neural network (ABB-CNN) for breast cancer …
N Thakur, P Kumar, A Kumar - Neural Computing and Applications, 2024 - Springer
Breast cancer poses a threat to women's health and contributes to an increase in mortality
rates. Mammography has proven to be an effective tool for the early detection of breast …
rates. Mammography has proven to be an effective tool for the early detection of breast …
Classification of Freshwater Fish Diseases in Bangladesh Using a Novel Ensemble Deep Learning Model: Enhancing Accuracy and Interpretability
A Al Maruf, SH Fahim, R Bashar, RA Rumy… - IEEE …, 2024 - ieeexplore.ieee.org
Effective disease management and mitigation strategies for fish diseases depend on timely
and accurate diagnosis. In recent years, artificial intelligence methods—classification …
and accurate diagnosis. In recent years, artificial intelligence methods—classification …
PLA—A Privacy-Embedded Lightweight and Efficient Automated Breast Cancer Accurate Diagnosis Framework for the Internet of Medical Things
The Internet of Medical Things (IoMT) can automate breast tumor detection and classification
with the potential of artificial intelligence. However, the leakage of sensitive data can cause …
with the potential of artificial intelligence. However, the leakage of sensitive data can cause …
Enhancing early breast cancer diagnosis through automated microcalcification detection using an optimized ensemble deep learning framework
Background Breast cancer remains a pressing global health concern, necessitating accurate
diagnostics for effective interventions. Deep learning models (AlexNet, ResNet-50, VGG16 …
diagnostics for effective interventions. Deep learning models (AlexNet, ResNet-50, VGG16 …