An extensive review of state-of-the-art transfer learning techniques used in medical imaging: Open issues and challenges
Deep learning techniques, which use a massive technology known as convolutional neural
networks, have shown excellent results in a variety of areas, including image processing …
networks, have shown excellent results in a variety of areas, including image processing …
AI-based automatic detection and classification of diabetic retinopathy using U-Net and deep learning
A Bilal, L Zhu, A Deng, H Lu, N Wu - Symmetry, 2022 - mdpi.com
Artificial intelligence is widely applied to automate Diabetic retinopathy diagnosis. Diabetes-
related retinal vascular disease is one of the world's most common leading causes of …
related retinal vascular disease is one of the world's most common leading causes of …
Intelligent hybrid deep learning model for breast cancer detection
Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most
common cancers in women. Women are also at risk from BC, the second most life …
common cancers in women. Women are also at risk from BC, the second most life …
A survey on human cancer categorization based on deep learning
In recent years, we have witnessed the fast growth of deep learning, which involves deep
neural networks, and the development of the computing capability of computer devices …
neural networks, and the development of the computing capability of computer devices …
A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images
Diabetic retinopathy (DR) is an ocular manifestation of diabetes and the leading cause of
visual impairment and blindness across the globe. Early detection and treatment of DR can …
visual impairment and blindness across the globe. Early detection and treatment of DR can …
Breast cancer mammograms classification using deep neural network and entropy-controlled whale optimization algorithm
Breast cancer has affected many women worldwide. To perform detection and classification
of breast cancer many computer-aided diagnosis (CAD) systems have been established …
of breast cancer many computer-aided diagnosis (CAD) systems have been established …
Boosted efficientnet: Detection of lymph node metastases in breast cancer using convolutional neural networks
Simple Summary The assistance of computer image analysis that automatically identifies
tissue or cell types has greatly improved histopathologic interpretation and diagnosis …
tissue or cell types has greatly improved histopathologic interpretation and diagnosis …
Classification of breast cancer histopathological images using DenseNet and transfer learning
Breast cancer is one of the most common invading cancers in women. Analyzing breast
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …
Breast cancer classification through meta-learning ensemble technique using convolution neural networks
This study aims to develop an efficient and accurate breast cancer classification model using
meta-learning approaches and multiple convolutional neural networks. This Breast …
meta-learning approaches and multiple convolutional neural networks. This Breast …
[HTML][HTML] Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images
S Mohapatra, S Muduly, S Mohanty… - Sustainable Operations …, 2022 - Elsevier
Breast cancer detection based on the deep learning approach has gained much interest
among other conventional-based CAD systems as the conventional based CAD system's …
among other conventional-based CAD systems as the conventional based CAD system's …