Researchers have reformulated multi-class classification as a many-objective problem in which each class is a separate goal, ...

There are effective approaches for 2D segmentation, yet 3D cell segmentation remains a critical bottleneck due to diverse cell morphologies, low signal-to-noise ratios, and data scarcity. We introduce ...

VM fleets, SaaS-only shops, MSP-managed SMBs — the right ransomware recovery pick depends on who you are. Eight choices ...

Researchers in Pakistan trained deep learning models to distinguish four histopathologically similar inflammatory skin ...

Researchers used machine learning-assisted laser-induced breakdown spectroscopy (LIBS) to classify PP, PET, HDPE, and LDPE under three laser-energy conditions designed to mimic variations in laser ...

Overview:  Deep learning uses multi-layer neural networks to learn patterns from data.CNNs, RNNs, LSTMs, transformers, and ...

Artificial intelligence (AI) is reshaping cardiovascular medicine, evolving from task-specific supervised models towards ...

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 ...

Abstract: There is a lot of hope that medical diagnostics could be improved by using deep learning intended for brain tumor categorization. In order to categorize brain malignancies, there are 4 ...

Transfer learning is a machine learning (ML) technique where an already developed ML model is reused in another task. Transfer learning is a popular approach in deep learning, as it enables the ...

Abstract: Diagnosing brain tumors is challenging for radiologists because of the significant similarities between the tumor types. Deep learning models lack sufficient data to effectively learn the ...