Browsing by Subject "convolutional neural network"
Now showing items 1-13 of 13
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Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application
(2020)Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding ... -
Deep Endoscopic Visual Measurements
(2019)Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extending to cover the increasing needs for accurate therapeutic interventions. ... -
Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease
(2022)Objective: The exploration and the implementation of a deep learning method using a state-of-the-art convolutional neural network for the classification of polar maps represent myocardial perfusion for the detection of ... -
Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip
(2022)Differential diagnosis between avascular necrosis (AVN) and transient osteoporosis of the hip (TOH) can be complicated even for experienced MSK radiologists. Our study attempted to use MR images in order to develop a deep ... -
Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images
(2022)(1) Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a long-established estimation methodology for medical diagnosis using image classification illustrating conditions ... -
Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy
(2020)Objective: The main aim of this work is to build a robust Convolutional Neural Network (CNN) algorithm that efficiently and quickly classifies bone scintigraphy images, by determining the presence or absence of prostate ... -
Differentiation between subchondral insufficiency fractures and advanced osteoarthritis of the knee using transfer learning and an ensemble of convolutional neural networks
(2022)Purpose: Subchondral insufficiency fractures (SIF) and advanced osteoarthritis (OA) of the knee are usually seen in conjunction with bone marrow lesions (BMLs) and their differentiation may pose a significant diagnostic ... -
Efficient bone metastasis diagnosis in bone scintigraphy using a fast convolutional neural network architecture
(2020)(1) Background: Bone metastasis is among diseases that frequently appear in breast, lung and prostate cancer; the most popular imaging method of screening in metastasis is bone scintigraphy and presents very high sensitivity ... -
Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review
(2022)The improved treatment of knee injuries critically relies on having an accurate and costeffective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim ... -
Machine learning for rhabdomyosarcoma histopathology
(2022)Correctly diagnosing a rare childhood cancer such as sarcoma can be critical to assigning the correct treatment regimen. With a finite number of pathologists worldwide specializing in pediatric/young adult sarcoma ... -
Machine Learning in Meningioma MRI: Past to Present. A Narrative Review
(2022)Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it ... -
Orchard mapping with deep learning semantic segmentation
(2021)This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection ... -
Pose recognition using convolutional neural networks on omni-directional images
(2018)Convolutional neural networks (CNNs) are used frequently in several computer vision applications. In this work, we present a methodology for pose classification of binary human silhouettes using CNNs, enhanced with image ...