… performing deep learning model … deep learning in the automated detection of PD, in the
hopes of improving the utility, applicability and impact of such tools to improve early detection …

… Early detection of BC allows patients to receive … new deep-learning (DL) model based on
the transfer-learning (TL) technique is developed to efficiently assist in the automatic detection …

… Deep learning … of deep learning models in the detection of Alzheimer's Disease (AD). The
main objective of this research is to analyze different deep learning methods used for detecting …

… using the same external MESSIDOR 2 data set as Gulshan et al, that group detected only …
to detect mild DR. Given our results, there is a high potential for automated machine learning …

… a deep layered CNN with transfer learning on discriminant color space for the recognition …
-ary, 3-ary and 4-ary classification models. They are tuned to perform optimally on a training …

… (AI) assisted automated detection of lung infections may … In this paper, we propose a
new method for detecting COVID-… extraction methods and trained deep neural networks (DNN) …

… Our proposed deep learning model is designed to detect disease in leaf images taken in
real time. Machine learning methods and direct Faster CNN models were not able to yield such …

… the basis of severity level using a deep learning model. This paper proposes a deep
learning-based automated detection and classification model for fundus DR images. The proposed …

… a method for automatically detecting and counting productive tillers of rice crop was proposed
based on deep learning convolutional neural network (CNN). The CNN model was trained …

… an automated traffic accident detection approach becomes desirable for computer vision
researchers. Nowadays, Deep Learning (… an automated DL-based method capable of detecting …