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Found 7 papers, 0 papers with code
Estimation of Acetabular Version from Anteroposterior Pelvic Radiograph Employing Deep Learning
The predicted errors in the estimation of right and left angles using the proposed method of deep learning are in the accurate region of error (<=3 degrees) which shows the ability of the proposed method in measuring anatomical version based on AP images.
Multi-Scale Convolutional Neural Network for Automated AMD Classification using Retinal OCT Images
The workload of specialists and the healthcare system in this field has increased in recent years mainly due to the prevalence of population aging worldwide and the chronic nature of AMD.
Automatic Ship Classification Utilizing Bag of Deep Features
In this paper, by applying the "bag of words" (BoW), a new method is presented that its words are the features that are obtained using pre-trained models of deep convolutional networks.
Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks
The proposed model in this study outperformed other modified VGG16 architectures with the accuracy of 93% and also the results are competitive with other state of the art frameworks for classification of breast cancer lesions.
Accurate Stress Assessment based on functional Near Infrared Spectroscopy using Deep Learning Approach
Employment of the proposed deep learning system trained on the fNIRS measurements leads to higher stress classification accuracy than the existing methods proposed in fNIRS studies in which the same experimental procedure has been employed.
Region-based Convolution Neural Network Approach for Accurate Segmentation of Pelvic Radiograph
With the increasing usage of radiograph images as a most common medical imaging system for diagnosis, treatment planning, and clinical studies, it is increasingly becoming a vital factor to use machine learning-based systems to provide reliable information for surgical pre-planning.
Estimation of Pelvic Sagittal Inclination from Anteroposterior Radiograph Using Convolutional Neural Networks: Proof-of-Concept Study
Alignment of the bones in standing position provides useful information in surgical planning.
