Deep learning has revolutionised image classification by enabling computational models to learn hierarchical representations directly from raw pixel data. Central to these advances are convolutional ...

In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classification of citrus diseases from images. Extensive ...

AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...

Conceived an international research group, the proposed model uses the convolutional neural network (CNN) architecture U-Net for image segmentation and the the CNN architecture InceptionV3-Net for ...

Researchers have leveraged deep learning techniques to enhance the image quality of a metalens camera. The new approach uses artificial intelligence to turn low-quality images into high-quality ones, ...

Deep learning high-content imaging is rapidly reshaping image-based screening in the modern laboratory environment. As high-content screening (HCS) generates increasingly large and complex datasets, ...

Explore how AI phenotypic screening transforms image-based drug discovery through advanced phenotypic data analysis and ...

A new publication from Opto-Electronic Advances, 10.29026/oea.2024.230034 discusses how the synergy of traditional techniques and deep learning enables single-frame high-precision fringe pattern ...