Background and objectives Artificial intelligence (AI) has branched out to various
applications in healthcare, such as health services management, predictive medicine …

Deep learning-based methods, in particular, convolutional neural networks and fully
convolutional networks are now widely used in the medical image analysis domain. The …

The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly
improved by an early and accurate diagnosis. Several studies have created automated …

One of the most promising research areas in the healthcare industry and the scientific
community is focusing on the AI-based applications for real medical challenges such as the …

Background: This study targets the development of an explainable deep learning
methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI …

Background In deep learning the most significant breakthrough in the field of image
recognition, object detection language processing was done by Convolutional Neural …

Electrical impedance tomography (EIT) has been widely used in biomedical research
because of its advantages of real-time imaging and nature of being non-invasive and …

Recent advances in deep learning led to several algorithms for the accurate diagnosis of
pneumonia from chest X-rays. However, these models require large training medical …

Accurate segmentation of lung nodules is an important basis for the subsequent
differentiation of benign and malignant pathological types, which is conducive to early …

Photoacoustic imaging is an emerging biomedical imaging technique that combines optical
contrast and ultrasound resolution to create unprecedented light absorption contrast in deep …