Effect of Data Augmentation of F-18-Florbetaben Positron-Emission Tomography Images by Using Deep Learning Convolutional Neural Network Architecture for Amyloid Positive Patients

2019 ◽  
Vol 75 (8) ◽  
pp. 597-604
Author(s):  
Hyun Jin Yoon ◽  
Young Jin Jeong ◽  
Do-Young Kang ◽  
Hyun Kang ◽  
Kang Kuk Yeo ◽  
...  
Keyword(s):  
Neural Network ◽  
Positron Emission Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Data Augmentation ◽  
Emission Tomography ◽  
Neural Network Architecture ◽  
Positron Emission

PS02.070: DEEP NEURAL NETWORK TO PREDICT POOR PROGNOSTIC FACTORS IN PATIENTS WITH ESOPHAGEAL CANCER

2018 ◽  
Vol 31 (Supplement_1) ◽  
pp. 140-140
Author(s):  
Po-Kuei Hsu ◽  
Joe Yeh
Keyword(s):  
Neural Network ◽  
Positron Emission Tomography ◽  
Esophageal Cancer ◽  
Prognostic Factors ◽  
Convolutional Neural Network ◽  
Tumor Cells ◽  
Lymphovascular Invasion ◽  
Perineural Invasion ◽  
Emission Tomography ◽  
Positron Emission

Abstract Background Both lymphovascular invasion, which is characterized by penetration of tumor cells into the peritumoural vascular or lymphatic network, and perineural invasion, which is characterized by involvement of tumor cells surrounding nerve fibers, are considered as an important step for tumor spreading, and are known poor prognostic factors in esophageal cancer. However, the information of these histological features is unavailable until pathological examination of surgical resected specimens. We aim to predict the presence or absence of these factors by positron emission tomography images during staging workup. Methods The positron emission tomography images before treatment and pathological reports of 278 patients who underwent esophagectomy for squamous cell carcinoma were collected. Stepwise convolutional neural network was constructed to distinguish patient with either lymphovascular invasion or perineural invasion from those without. Results Randomly selected 248 patients were included in the testing set. Stepwise approach was used in training our custom neural network. The performance of fine-tuned neural network was tested in another independent 30 patients. The accuracy rate of predicting the presence or absence of either lymphovascular invasion or perineural invasion was 66.7% (20 of 30 were accurate). Conclusion Using pre-treatment positron emission tomography images alone to predict the presence of absence of poor prognostic histological factors, i.e. lymphovascular invasion or perineural invasion, with deep convolutional neural network is possible. The technique of deep learning may identify patients with poor prognosis and enable personalized medicine in esophageal cancer. Disclosure All authors have declared no conflicts of interest.

scholarly journals AMBIENT: Accelerated Convolutional Neural Network Architecture Search for Regulatory Genomics

2021 ◽  
Author(s):  
Zijun Zhang ◽  
Evan M. Cofer ◽  
Olga G. Troyanskaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Environmental Issue ◽  
Biological Sequences ◽  
Neural Network Architecture ◽  
Computing Power ◽  
Neural Architecture

Convolutional neural networks (CNN) have become a standard approach for modeling genomic sequences. CNNs can be effectively built by Neural Architecture Search (NAS) by trading computing power for accurate neural architectures. Yet, the consumption of immense computing power is a major practical, financial, and environmental issue for deep learning. Here, we present a novel NAS framework, AMBIENT, that generates highly accurate CNN architectures for biological sequences of diverse functions, while substantially reducing the computing cost of conventional NAS.

Malicious Code Invariance Based On Deep Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 1-7
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Malicious Code ◽  
Neural Network Architecture ◽  
Security Issues ◽  
The Neural Network ◽  
Code Identification ◽  
Malicious Code Detection

The malicious code detection is critical task for in the field of security. The malicious code detection can be possibly by using convolutional neural network (CNN).Themalicious code can be categorized in to different families. The malicious code identification helps to identify the affected malware on the system. Malicious code theft data from our system and it yields high security issues in real time. The neural network architecture classifies the malicious code based on the collected dataset. The dataset contains different families of malicious code. The malicious code detection can be done with the help of model created from CNN architecture

scholarly journals Disease Detection in Tomato Plants Using Deep Learning

2020 ◽  
Author(s):  
Marimuthu S ◽  
Bhuvana J ◽  
Mirnalinee T T
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Soil Quality ◽  
Tomato Plant ◽  
Network Architecture ◽  
Disease Detection ◽  
Neural Network Architecture ◽  
Disease Category ◽  
Tomato Plants

Agriculture is the backbone of the economy of any country. Productivity of the crops depends on soil quality, proper irrigation and fertilizer, appropriate pesticide. Mostly pesticides were applied without having knowledge about the type of diseases or pests. Type and the quantity of pesticide for any depends on the disease category. If we could identify the appropriate disease, then applying appropriate pesticide will increase the yield of the crop. To address this issue, we propose a method to detect the diseases in tomato plants. We have designed a Convolutional Neural Network architecture that efficiently detects the disease of tomato plant. The system is evaluated with Plant Village benchmark dataset. Results show that our network is detecting the diseases with 90.86% accuracy. We have identified a suitable variant of Convolutional Neural Network that efficiently detects the disease of tomato plant.

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