Abstract Machine vision is being employed in defect detection, size measurement, pattern
recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection …

In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is
proposed for cancer detection from histopathology images. To build a highly generalized …

Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a
necessary first step. AI development benefits from large sample sizes and diverse cohorts …

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide.
In coping with it, histopathology image analysis (HIA) provides key information for clinical …

Image segmentation remains to be one of the most vital tasks in the area of computer vision
and more so in the case of medical image processing. Image segmentation quality is the …

Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital
pathology is essential for assessing prognosis. Due to the increasing resolution and quantity …

In pathology, tissue samples are assessed using multiple staining techniques to enhance
contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN …

The diagnosis of different types of cancer, including oral cavity-derived cancer, is made by a
pathologist through complex and time-consuming microscopic analysis of tissue samples …

Cardiovascular malformations and diseases are common but complex and often not yet fully
understood. To better understand the effects of structural and microstructural changes of the …

Simple Summary In this study, we established four convolutional neural network (DCNN)
models (AlexNet, ResNet101, DenseNet201, and InceptionV3) to predict drug-sensitive …