Exploring Deep Convolution Neural Networks with Transfer Learning for Transformation Zone Type Prediction in Cervical Cancer

2020 ◽  
pp. 1127-1138
Author(s):  
Mamta Arora ◽  
Sanjeev Dhawan ◽  
Kulvinder Singh
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Transfer Learning ◽  
Transformation Zone ◽  
Convolution Neural Networks

Exploring Deep Convolution Neural Networks with Transfer Learning for Rectum Toxicity Prediction in Cervical Cancer Radiotherapy

Brachytherapy ◽  
2017 ◽  
Vol 16 (3) ◽  
pp. S33-S34 ◽  
Author(s):  
Xin Zhen ◽  
Jiawei Chen ◽  
Zichun Zhong ◽  
Brian Hrycushko ◽  
Steve Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Transfer Learning ◽  
Toxicity Prediction ◽  
Cancer Radiotherapy ◽  
Convolution Neural Networks

Deep Transfer Learning for Histopathological Diagnosis of Cervical Cancer Using Convolutional Neural Networks with Visualization Schemes

2020 ◽  
Vol 10 (2) ◽  
pp. 391-400 ◽  
Author(s):  
Ying Chen ◽  
Xiaomin Qin ◽  
Jingyu Xiong ◽  
Shugong Xu ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Disease Classification ◽  
Natural Image ◽  
Histopathological Diagnosis ◽  
The Impact

This study aimed to propose a deep transfer learning framework for histopathological image analysis by using convolutional neural networks (CNNs) with visualization schemes, and to evaluate its usage for automated and interpretable diagnosis of cervical cancer. First, in order to examine the potential of the transfer learning for classifying cervix histopathological images, we pre-trained three state-of-the-art CNN architectures on large-size natural image datasets and then fine-tuned them on small-size histopathological datasets. Second, we investigated the impact of three learning strategies on classification accuracy. Third, we visualized both the multiple-layer convolutional kernels of CNNs and the regions of interest so as to increase the clinical interpretability of the networks. Our method was evaluated on a database of 4993 cervical histological images (2503 benign and 2490 malignant). The experimental results demonstrated that our method achieved 95.88% sensitivity, 98.93% specificity, 97.42% accuracy, 94.81% Youden's index and 99.71% area under the receiver operating characteristic curve. Our method can reduce the cognitive burden on pathologists for cervical disease classification and improve their diagnostic efficiency and accuracy. It may be potentially used in clinical routine for histopathological diagnosis of cervical cancer.

Hierarchical deep convolution neural networks based on transfer learning for transformer rectifier unit fault diagnosis

Measurement ◽  
2021 ◽  
Vol 167 ◽  
pp. 108257
Author(s):  
Shuwen Chen ◽  
Hongjuan Ge ◽  
Huang Li ◽  
Youchao Sun ◽  
Xiaoyan Qian
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Convolution Neural Networks

scholarly journals Parking Assist using Convolution Neural Networks

2020 ◽  
Vol 9 (6) ◽  
pp. 248-252
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Transfer Learning ◽  
Waiting Time ◽  
Urban Areas ◽  
Parking Space ◽  
Convolution Neural Networks ◽  
Machine Learning Model ◽  
Parking System

Parking vehicles are one of the most frustrating tasks that people face these days. Locating an available parking space is a huge headache especially in urban areas. This paper aims to design one such parking system which, in many ways reduces the hassles of parking. The paper presents a system where a Machine Learning model, Convolution Neural Network(CNN) is used to classify parking slots in a parking space into vacant and filled slots. In order to optimize the task of classification, the method of Transfer Learning is implemented in the paper. The problem of parking stands not only limited to causing inconvenience to the drivers, but also escalates to much larger and extensive problems, affecting a lot more people the environment. Hence it is very important to have a system is used parking system in place. The model proposed in the paper sends across parking information to a driver well in advance, there by greatly reducing the waiting time for the vehicle.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution
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