Deep learning‐based classification of rectal fecal retention and analysis of fecal properties using ultrasound images in older adult patients

2020 ◽  
Vol 17 (4) ◽  
Author(s):  
Masaru Matsumoto ◽  
Takuya Tsutaoka ◽  
Gojiro Nakagami ◽  
Shiho Tanaka ◽  
Mikako Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Older Adult ◽  
Adult Patients ◽  
Ultrasound Images

scholarly journals OP01.09: Automatic classification of fetal ultrasound images: a remarkable application of deep learning technology for fetal medicine

2019 ◽  
Vol 54 (S1) ◽  
pp. 86-87
Author(s):  
X.P. Burgos‐Artizuu ◽  
E. Eixarch ◽  
D. Coronado‐Gutierrez ◽  
B. Valenzuela ◽  
E. Bonet‐Carne ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Fetal Ultrasound ◽  
Fetal Medicine

scholarly journals Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning

2020 ◽  
Vol 9 (7) ◽  
pp. 1211-1215
Keyword(s):  
Deep Learning ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Second Opinion ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Cad System ◽  
Improve Accuracy

Ultrasound scanning is most excellent significant diagnosis techniques utilized for thyroid nodules identification. A thyroid nodule is unnecessary cells that can develop in your base of neck which can be normal or cancerous. Many Computer added diagnosis systems (CAD) have been developed as a second opinion for radiologist. The thyroid nodules classification using machine learning and deep learning approach is latest trend which is using to improve accuracy for differentiation of thyroid nodules from benign and malignant type. In this paper we review the most recent work on CAD system which uses different feature extraction technique and classifier used for thyroid nodules classification with deep learning approach. This paper we illustrate the result obtained by these studies and highlight the limitation of each proposed methods. Moreover we summarize convolution neural network (CNN) architecture for classification of thyroid nodule. This literature review is meant at researcher but it also useful for radiologist who is interesting in CAD tool in ultrasound imaging for second opinion.

scholarly journals Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

2021 ◽  
pp. 29-42
Author(s):  
admin admin ◽  
◽  
◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Ultrasound Image ◽  
Smart Devices ◽  
Learning Approach ◽  
Ultrasound Images ◽  
Medical Field ◽  
Modern Methods

With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.

scholarly journals Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images

2019 ◽  
Vol 38 (3) ◽  
pp. 762-774 ◽  
Author(s):  
Seung Yeon Shin ◽  
Soochahn Lee ◽  
Il Dong Yun ◽  
Sun Mi Kim ◽  
Kyoung Mu Lee
Keyword(s):  
Deep Learning ◽  
Breast Ultrasound ◽  
Ultrasound Images

Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning

2019 ◽  
Vol 43 (8) ◽  
Author(s):  
Serkan Savaş ◽  
Nurettin Topaloğlu ◽  
Ömer Kazcı ◽  
Pınar Nercis Koşar
Keyword(s):  
Deep Learning ◽  
Carotid Artery ◽  
Intima Media Thickness ◽  
Ultrasound Images ◽  
Media Thickness

scholarly journals Detection and Classification of Pregnancy StateUsing Deep Learning Technique

2021 ◽  
Vol 17 (2) ◽  
pp. 71-85
Author(s):  
Hassan Abdelrhman Mohammed ◽  
Eltahir Mohmmed Hussein ◽  
Mahir Mohammed Sharif
Keyword(s):  
Deep Learning ◽  
Health Status ◽  
Fetal Development ◽  
Processing System ◽  
Ultrasound Images ◽  
Fetal Health ◽  
Proposed Model ◽  
Learning Technique ◽  
Healthy Growth

This work  aims to design and develop a model that detects and classifies pregnancy health status. Ultrasound is one of the most prevalent developments in clinical imaging, as it enables a doctor to evaluate, analyze and treat diseases. Most complications from pregnancy lead to serious problems that restrict healthy growth, causing weakness or death. In this work, an image processing system was developed to recognize the  health during pregnancy and classify it for all stages of its development. The technique in deep learning has been implemented, as CNN (Resnet50) image recognition model was applied to detect and classify fetal health status from ultrasound images. The proposed model contributed to providing an integrated solution for each pregnancy period that works to identify all stages of fetal development, starting from the pre-pregnancy stage (here it is known about the suitability of the uterus for pregnancy, the size of the ovum, and its ability to form the fetus) and up to the stage of birth, through training, verification and testing using the cross-verification technique that five folds of the diagnostic rudder were used under the patterns that distinguish each stage from the other and to verify that it is sound or unsound in the concerning stage. This study enhanced diagnostic accuracy by using transfer learning and novel accessory images that were not trained as feedback. The model achieved an accuracy of 96.5% in detecting the fetus and classifying it into any of the stages that were divided according to the features that appear from one stage to the next to eleven categories.  

scholarly journals Correction to: Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning

2019 ◽  
Vol 43 (9) ◽  
Author(s):  
Serkan Savaş ◽  
Nurettin Topaloğlu ◽  
Ömer Kazcı ◽  
Pınar Nercis Koşar
Keyword(s):  
Deep Learning ◽  
Carotid Artery ◽  
Intima Media Thickness ◽  
Ultrasound Images ◽  
Media Thickness

scholarly journals VP25.02: Automatic classification of fetal axial brain planes from ultrasound images via deep learning

2020 ◽  
Vol 56 (S1) ◽  
pp. 162-162
Author(s):  
D. Coronado‐Gutierrez ◽  
X.P. Burgos‐Artizuu ◽  
E. Monterde ◽  
E. Eixarch ◽  
B. Valenzuela ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Ultrasound Images
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