… Alquhayz, “Multi view feature fusion based four views model for mammogram classification
using convolutional neural network,” IEEE Access, vol. 7, pp. 165724–165733, 2019. …

… , which gave rise to the deep learning techniques because of the unique properties to dwell
with larger datasets [12, 21]. The enlarged size of the histopathological images consists of …

… So in this work, the successes of deep learning methods in segmentation, classification,
cell structure and fracture detection, computer-aided identification, and GANs in synthetic …

… [59] adopted an attention-guided GAN [60] to harmonize images from three publicly available
brain MRI datasets to generate fake images. Then, a 2D AlexNet CNN model was used to …

Deep learning (DL) has seen an exponential development in recent years, with major impact
in many medical fields, especially in the field of medical image. The purpose of the work …

… found on arXiv, multi-view object counting papers, … dilated convolutional layers before
producing a density map estimate. The goal of this method is to use dilated convolutions to learn …

… In [81], the efficient spatial pyramid of dilated convolutions (ESPnet) is proposed, which
has … and background stuff have been dealt together in attention guided unified network (AUNet). …

Accepted: 12 January 2022 Deep Neural networks algorithms are recently used to solve
problems in medical imaging like no time ever. However, one of the main challenges for training …

… to test automated retinal disease classification. In this work, a number of automated strategies
for decision and classification problems use machine learning and reinforcement learning …

… dilated convolution layers with a kernel of (3×3) with dilation value d, batch normalization, and
ReLU activation. In our architecture, n = 3 and d = 2. The dilated … multi-view deep learning. …