To our knowledge, this study is the first to apply deep learning models that can, beyond diagnosis, identify molecular subtypes and predict outcomes in a single brain tumour entity (meningioma) using ...

Deep transfer learning has emerged as a powerful paradigm in image classification, enabling models to leverage knowledge acquired from large, labelled datasets to perform effectively on new tasks with ...

Deep learning techniques have been successfully applied to object classification in Synthetic Aperture Radar (SAR) images, achieving remarkable performance. However, the current Transformer ...

aTaub Faculty of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel bFaculty of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, Israel ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

This repository contains Python notebooks demonstrating image classification using Azure AutoML for Images. These notebooks provide practical examples of building computer vision models for various ...

White Blood Cell Classification is a deep learning project built with Python, TensorFlow, and Keras that classifies five types of WBCs from microscopic images using a CNN model. With advanced image ...

This study aimed to develop a hybrid deep learning model for classifying multiple fundus diseases using ultra-widefield (UWF) images, thereby improving diagnostic efficiency and accuracy while ...

The Tapenade pipeline enables single-cell, whole-mount 3D quantification in dense multilayer organoids, linking spatial gene co-expression and nuclear deformation to emerging tissue-scale organization ...

Abstract: Effective classification of plant diseases is crucial for increasing agricultural productivity and ensuring global food se-curity. Deep learning, in particular convolutional neural networks ...