Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study
Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …
Convolutional neural networks for radiologic images: a radiologist's guide
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …
recently gained particular attention in the radiology community. This article provides an …
[HTML][HTML] Deep learning in food category recognition
Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …
research for the past few decades. It is potentially one of the next steps in revolutionizing …
Stochastic dilated residual ghost model for breast cancer detection
R Kashyap - Journal of Digital Imaging, 2023 - Springer
One of the most contentious issues in modern medicine is how to effectively standardise
breast cancer screening. Deep learning models are already saving lives in the medical field …
breast cancer screening. Deep learning models are already saving lives in the medical field …
Classification of Alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling
SH Wang, P Phillips, Y Sui, B Liu, M Yang… - Journal of medical …, 2018 - Springer
Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide
a new computer-vision based technique to detect it in an efficient way. The brain-imaging …
a new computer-vision based technique to detect it in an efficient way. The brain-imaging …
Deep learning based diagnosis of Parkinson's disease using convolutional neural network
S Sivaranjini, CM Sujatha - Multimedia tools and applications, 2020 - Springer
Parkinson's disease is the second most common degenerative disease caused by loss of
dopamine producing neurons. The substantia nigra region is deprived of its neuronal …
dopamine producing neurons. The substantia nigra region is deprived of its neuronal …
Deep neural networks in psychiatry
Abstract Machine and deep learning methods, today's core of artificial intelligence, have
been applied with increasing success and impact in many commercial and research …
been applied with increasing success and impact in many commercial and research …
Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU
Multiple sclerosis is a condition affecting brain and/or spinal cord. Based on deep learning,
this study aims to develop an improved convolutional neural network system. We collected …
this study aims to develop an improved convolutional neural network system. We collected …
Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization
SH Wang, K Muhammad, J Hong, AK Sangaiah… - Neural Computing and …, 2020 - Springer
Alcoholism changes the structure of brain. Several somatic marker hypothesis network-
related regions are known to be damaged in chronic alcoholism. Neuroimaging approach …
related regions are known to be damaged in chronic alcoholism. Neuroimaging approach …
[HTML][HTML] Deep learning intervention for health care challenges: some biomedical domain considerations
The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care
problems has received unprecedented attention in the last decade. The technique has …
problems has received unprecedented attention in the last decade. The technique has …