In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset
for handwritten digit recognition. This dataset has been extensively used to validate novel …

Deep learning (DL)-based semantic segmentation methods have been providing state-of-
the-art performance in the past few years. More specifically, these techniques have been …

Abstract The Deep Convolutional Neural Network (DCNN) is one of the most powerful and
successful deep learning approaches. DCNNs have already provided superior performance …

U-Net has been providing state-of-the-art performance in many medical image segmentation
problems. Many modifications have been proposed for U-Net, such as attention U-Net …

Bio-medical image segmentation is one of the promising sectors where nuclei segmentation
from high-resolution histopathological images enables extraction of very high-quality …

Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …

Clinical diagnosis of breast cancer is a challenging problem in the biomedical domain. The
BreakHis breast cancer histopathological image dataset consists of two classes: Benign …

Abstract Machine learning (ML) and cloud computing are modern and fast-growing areas
across all domains of applications. In agriculture, automated detection and diagnosis of …

Convolutional neural networks (CNNs) have been used over the past years to solve many
different artificial intelligence (AI) problems, providing significant advances in some domains …