Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …

The approach of using more than one processor to compute in order to overcome the
complexity of different medical imaging methods that make up an overall job is known as …

Background Due the computational complexity of sequence alignment algorithms, various
accelerated solutions have been proposed to speedup this analysis. NVBIO is the only …

Deep neural networks (DNNs) have been successfully deployed in widespread domains,
including healthcare applications. DenseNet201 is a new DNN architecture used in …

Hyperspectral imaging (HSI) has become a very compelling technique in different scientific
areas; indeed, many researchers use it in the fields of remote sensing, agriculture, forensics …

The proposed work develops a rapid and automatic method for brain tumour detection and
segmentation using multi-sequence magnetic resonance imaging (MRI) datasets available …

The aim of this article is to promote the development of rural finance and the further
informatization of rural banks. Based on DL (deep learning) and artificial intelligence …

Background and Objective B-spline interpolation (BSI) is a popular technique in the context
of medical imaging due to its adaptability and robustness in 3D object modeling. A field that …

Early diagnosis is considered important for medical images of breast cancer, the rate of
recovery and safety of affected women can be improved. It is also assisting doctors in their …