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 Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

scholarly journals Deep Learning Assisted Covid-19 Detection using full CT-scans

2020 ◽  
Author(s):  
varan singhrohila ◽  
Nitin Gupta ◽  
Amit Kaul ◽  
Deepak Sharma
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
Detection Accuracy ◽  
Residual Network ◽  
Excellent Performance ◽  
Examination Procedure ◽  
Medical Institutions ◽  
Proposed Model ◽  
Learning Technique ◽  
Intensive Approach

<div>The ongoing pandemic of COVID-19 has shown</div><div>the limitations of our current medical institutions. There</div><div>is a need for research in the field of automated diagnosis</div><div>for speeding up the process while maintaining accuracy</div><div>and reducing computational requirements. In this work, an</div><div>automatic diagnosis of COVID-19 infection from CT scans</div><div>of the patients using Deep Learning technique is proposed.</div><div>The proposed model, ReCOV-101 uses full chest CT scans to</div><div>detect varying degrees of COVID-19 infection, and requires</div><div>less computational power. Moreover, in order to improve</div><div>the detection accuracy the CT-scans were preprocessed by</div><div>employing segmentation and interpolation. The proposed</div><div>scheme is based on the residual network, taking advantage</div><div>of skip connection, allowing the model to go deeper.</div><div>Moreover, the model was trained on a single enterpriselevel</div><div>GPU such that it can easily be provided on the edge of</div><div>the network, reducing communication with the cloud often</div><div>required for processing the data. The objective of this work</div><div>is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can</div><div>be combined with medical equipment and help ease the</div><div>examination procedure. Moreover, with the proposed model</div><div>an accuracy of 94.9% was achieved.</div>

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 Classification of Stages Diabetic Retinopathy Using MobileNetV2 Model

10.29007/h46n ◽  
2022 ◽  
Author(s):  
Hoang Nhut Huynh ◽  
Minh Thanh Do ◽  
Gia Thinh Huynh ◽  
Anh Tu Tran ◽  
Trung Nghia Tran
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
High Performance ◽  
Vision Loss ◽  
Training Model ◽  
Average Accuracy ◽  
Common Diagnosis ◽  
Learning Technique ◽  
The Common

Diabetic retinopathy (DR) is a complication of diabetes mellitus that causes retinal damage that can lead to vision loss if not detected and treated promptly. The common diagnosis stages of the disease take time, effort, and cost and can be misdiagnosed. In the recent period with the explosion of artificial intelligence, deep learning has become the most popular tool with high performance in many fields, especially in the analysis and classification of medical images. The Convolutional Neural Network (CNN) is more widely used as a deep learning method in medical imaging analysis with highly effective. In this paper, the five-stage image of modern DR (healthy, mild, moderate, severe, and proliferative) can be detected and classified using the deep learning technique. After cross-validation training and testing on the corresponding 5,590-image dataset, a pre-MobileNetV2 training model is proposed in classifying stages of diabetic retinopathy. The average accuracy of the model achieved was 93.89% with the precision of 94.00%, recall 92.00% and f1-score 90.00%. The corresponding thermal image is also given to help experts for evaluating the influence of the retina in each different stage.

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 Visual Detection for Android Malware using Deep Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 152-156
Keyword(s):  
Deep Learning ◽  
Main Idea ◽  
Mobile Internet ◽  
Android Application ◽  
Analysis Model ◽  
Android Malware ◽  
Proposed Model ◽  
Learning Technique ◽  
Android Applications ◽  
High Level

The most serious threats to the current mobile internet are Android Malware. In this paper, we proposed a static analysis model that does not need to understand the source code of the android applications. The main idea is as most of the malware variants are created using automatic tools. Also, there are special fingerprint features for each malware family. According to decompiling the android APK, we mapped the Opcodes, sensitive API packages, and high-level risky API functions into three channels of an RGB image respectively. Then we used the deep learning technique convolutional neural network to identify Android application as benign or as malware. Finally, the proposed model succeeds to detect the entire 200 android applications (100 benign applications and 100 malware applications) with an accuracy of over 99% as shown in experimental results.

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