Diagnostic strategies for breast cancer detection: from image generation to classification strategies using artificial intelligence algorithms
JA Basurto-Hurtado, IA Cruz-Albarran… - Cancers, 2022 - mdpi.com
Simple Summary With the recent advances in the field of artificial intelligence, it has been
possible to develop robust and accurate methodologies that can deliver noticeable results in …
possible to develop robust and accurate methodologies that can deliver noticeable results in …
Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally.
Breast cancer detection needs accurate mammography interpretation and analysis, which is …
Breast cancer detection needs accurate mammography interpretation and analysis, which is …
A review on lung disease recognition by acoustic signal analysis with deep learning networks
Recently, assistive explanations for difficulties in the health check area have been made
viable thanks in considerable portion to technologies like deep learning and machine …
viable thanks in considerable portion to technologies like deep learning and machine …
An automated in-depth feature learning algorithm for breast abnormality prognosis and robust characterization from mammography images using deep transfer …
Simple Summary Diagnosing breast cancer masses and calcification clusters is crucial in
mammography, which reduces disease consequences and initiates treatment at an early …
mammography, which reduces disease consequences and initiates treatment at an early …
Investigation of Effectiveness of Shuffled Frog‐Lea** Optimizer in Training a Convolution Neural Network
S Baseri Saadi, N Tataei Sarshar… - Journal of …, 2022 - Wiley Online Library
One of the leading algorithms and architectures in deep learning is Convolution Neural
Network (CNN). It represents a unique method for image processing, object detection, and …
Network (CNN). It represents a unique method for image processing, object detection, and …
MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms
Abstract Background and objective Deep Learning models have emerged as a significant
tool in generating efficient solutions for complex problems including cancer detection, as …
tool in generating efficient solutions for complex problems including cancer detection, as …
[HTML][HTML] BCDNet: an optimized deep network for ultrasound breast cancer detection
Objectives Breast cancer is a common but deadly disease among women. Medical imaging
is an effective method to diagnose breast cancer, but manual image screening is time …
is an effective method to diagnose breast cancer, but manual image screening is time …
Breast cancer: toward an accurate breast tumor detection model in mammography using transfer learning techniques
Female breast cancer has now surpassed lung cancer as the most common form of cancer
globally. Although several methods exist for breast cancer detection and diagnosis …
globally. Although several methods exist for breast cancer detection and diagnosis …
Mammogram classification based on a novel convolutional neural network with efficient channel attention
Q Lou, Y Li, Y Qian, F Lu, J Ma - Computers in Biology and Medicine, 2022 - Elsevier
Early accurate mammography screening and diagnosis can reduce the mortality of breast
cancer. Although CNN-based breast cancer computer-aided diagnosis (CAD) systems have …
cancer. Although CNN-based breast cancer computer-aided diagnosis (CAD) systems have …
[PDF][PDF] Classification of breast cancer using ensemble filter feature selection with triplet attention based efficient net classifier.
BN Madhukar, SH Bharathi, MP Ashwin, A Imaging - Int. Arab J. Inf. Technol., 2024 - iajit.org
In medical imaging, the effective detection and classification of Breast Cancer (BC) is a
current research important task because of the still existing difficulty to distinguish …
current research important task because of the still existing difficulty to distinguish …