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Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review
Being a second most cause of mortality worldwide, cancer has been identified as a perilous
disease for human beings, where advance stage diagnosis may not help much in …
disease for human beings, where advance stage diagnosis may not help much in …
Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review
R Adam, K Dell'Aquila, L Hodges, T Maldjian… - Breast Cancer …, 2023 - Springer
Deep learning analysis of radiological images has the potential to improve diagnostic
accuracy of breast cancer, ultimately leading to better patient outcomes. This paper …
accuracy of breast cancer, ultimately leading to better patient outcomes. This paper …
Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance
Breast cancer (BC) is spreading more and more every day. Therefore, a patient's life can be
saved by its early discovery. Mammography is frequently used to diagnose BC. The …
saved by its early discovery. Mammography is frequently used to diagnose BC. The …
Systematic review of computing approaches for breast cancer detection based computer aided diagnosis using mammogram images
Breast cancer is one of the most prevalent types of cancer that plagues females. Mortality
from breast cancer could be reduced by diagnosing and identifying it at an early stage. To …
from breast cancer could be reduced by diagnosing and identifying it at an early stage. To …
[HTML][HTML] Image augmentation techniques for mammogram analysis
Research in the medical imaging field using deep learning approaches has become
progressively contingent. Scientific findings reveal that supervised deep learning methods' …
progressively contingent. Scientific findings reveal that supervised deep learning methods' …
Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey
Advances in deep learning networks, especially deep convolutional neural networks
(DCNNs), are causing remarkable breakthroughs in radiology and imaging sciences. These …
(DCNNs), are causing remarkable breakthroughs in radiology and imaging sciences. These …
Enhancing small medical dataset classification performance using GAN
Develo** an effective classification model in the medical field is challenging due to limited
datasets. To address this issue, this study proposes using a generative adversarial network …
datasets. To address this issue, this study proposes using a generative adversarial network …
ETECADx: Ensemble self-attention transformer encoder for breast cancer diagnosis using full-field digital X-ray breast images
Early detection of breast cancer is an essential procedure to reduce the mortality rate among
women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called …
women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called …
Deep ensemble transfer learning-based framework for mammographic image classification
This research intends to provide a method for clinical decision support systems that can
accurately classify benign and malignant mass from breast X-ray images. The model was …
accurately classify benign and malignant mass from breast X-ray images. The model was …
Breast lesion classification from mammograms using deep neural network and test-time augmentation
In order to effectively aid in the automatic classification of the breast cancer suspicious
region, a new deep-learning (DL) model built on the transfer-learning (TL) technique is …
region, a new deep-learning (DL) model built on the transfer-learning (TL) technique is …