scholarly journals A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

2020 ◽  
Vol 10 (2) ◽  
pp. 559 ◽  
Author(s):  
Vikash Chouhan ◽  
Sanjay Kumar Singh ◽  
Aditya Khamparia ◽  
Deepak Gupta ◽  
Prayag Tiwari ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Medical Center ◽  
Network Models ◽  
Ensemble Model ◽  
X Rays ◽  
Neural Network Models ◽  
Learning Framework ◽  
Unseen Data ◽  
Chest X Ray ◽  
Bacteria And Fungi

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

scholarly journals Pneumonia Detection using Chest X-Ray Images

2021 ◽  
Vol 9 (VI) ◽  
pp. 1611-1618
Author(s):  
Shruti Meshram
Keyword(s):  
Medical Center ◽  
Network Models ◽  
Ensemble Model ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Learning Framework ◽  
Unseen Data ◽  
Chest X Ray ◽  
Bacteria And Fungi

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pre-trained on Image Net, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pre-trained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

scholarly journals Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 417 ◽  
Author(s):  
Mohammad Farukh Hashmi ◽  
Satyarth Katiyar ◽  
Avinash G Keskar ◽  
Neeraj Dhanraj Bokde ◽  
Zong Woo Geem
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Medical Center ◽  
Training Dataset ◽  
Test Accuracy ◽  
X Rays ◽  
Learning Models ◽  
Novel Approach ◽  
Unseen Data

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

scholarly journals Classification Of X-ray Images For Detecting Covid-19 Using Deep Transfer Learning

2020 ◽  
Author(s):  
Nidhi Bansal ◽  
S.Srid
Keyword(s):  
Transfer Learning ◽  
Network Models ◽  
World Health ◽  
Learning Potential ◽  
Neural Network Models ◽  
X Ray ◽  
Learning Framework ◽  
Health Organization ◽  
High Level

Abstract The coronavirus disease COVID-19 eruption is stated as a pandemic by the World Health Organization. It is affecting around 212 countries and territories across the globe. There is a need to constantly analyze and find patterns from lungs X-Ray images. Early diagnosis can constraint the exposure of person and aids to bound the feast of the virus. The manual diagnosis is quite tedious and time-consuming process. The main aim of this paper is to explore the transfer learning potential. A deep learning framework is proposed adopting the capability of pretrained Deep Convolutional Neural Network models with transfer learning. This assists in classification of the chest X-Ray Images with high level of accuracy. An analysis is done with utilization of six pretrained models – VGG16, VGG19, ResNet50V2, InceptionV3, Xception and NASNetLarge. The experiment results showed that the highest accuracy obtained was 97% using VGG16 and VGG19 with sensitivity and specificity of 100% and 94% respectively.

scholarly journals Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images

2021 ◽  
Vol 11 (3) ◽  
pp. 1242
Author(s):  
So-Mi Cha ◽  
Seung-Seok Lee ◽  
Bonggyun Ko
Keyword(s):  
Transfer Learning ◽  
Area Under The Curve ◽  
Attention Mechanism ◽  
Stable Model ◽  
X Rays ◽  
X Ray ◽  
Learning Framework ◽  
Computer Aided ◽  
Common Technique ◽  
Chest X Ray

Pneumonia is a form of acute respiratory infection commonly caused by germs, viruses, and fungi, and can prove fatal at any age. Chest X-rays is the most common technique for diagnosing pneumonia. There have been several attempts to apply transfer learning based on a Convolutional Neural Network to build a stable model in computer-aided diagnosis. Recently, with the appearance of an attention mechanism that automatically focuses on the critical part of the image that is crucial for the diagnosis of disease, it is possible to increase the performance of previous models. The goal of this study is to improve the accuracy of a computer-aided diagnostic approach that medical professionals can easily use as an auxiliary tool. In this paper, we proposed the attention-based transfer learning framework for efficient pneumonia detection in chest X-ray images. We collected features from three-types of pre-trained models, ResNet152, DenseNet121, ResNet18 as a role of feature extractor. We redefined the classifier for a new task and applied the attention mechanism as a feature selector. As a result, the proposed approach achieved accuracy, F-score, Area Under the Curve(AUC), precision and recall of 96.63%, 0.973, 96.03%, 96.23% and 98.46%, respectively.

scholarly journals Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework

2020 ◽  
Author(s):  
Shadman Sakib ◽  
Md. Abu Bakr Siddique ◽  
Mohammad Mahmudur Rahman Khan ◽  
Nowrin Yasmin ◽  
Anas Aziz ◽  
...  
Keyword(s):  
Transfer Learning ◽  
World Economy ◽  
X Rays ◽  
Test Dataset ◽  
X Ray ◽  
Learning Framework ◽  
Significant Step ◽  
Significant Performance ◽  
Normal Chest ◽  
Chest X Ray

AbstractWorld economy as well as public health have been facing a devastating effect caused by the disease termed as Coronavirus (COVID-19). A significant step of COVID-19 affected patient’s treatment is the faster and accurate detection of the disease which is the motivation of this study. In this paper, implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%. Most importantly, the model has shown a significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00 and Specificity = 1.00). Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection.

scholarly journals Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach

2021 ◽  
Vol 23 (3) ◽  
pp. 1681
Author(s):  
Aditya Rajbongshi ◽  
Thaharim Khan ◽  
Md. Mahbubur Rahman ◽  
Anik Pramanik ◽  
Shah Md Tanvir Siddiquee ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Image Data ◽  
Network Models ◽  
Plant Diseases ◽  
Prevention Measures ◽  
Neural Network Models ◽  
Data Set ◽  
Learning Techniques ◽  
Overall Performance ◽  
Mango Leaves

<p>The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.</p>

scholarly journals Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 207
Author(s):  
Asma Baccouche ◽  
Begonya Garcia-Zapirain ◽  
Cristian Castillo Olea ◽  
Adel Elmaghraby
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Ensemble Learning ◽  
Heart Diseases ◽  
Hypertensive Heart Disease ◽  
Network Models ◽  
Features Selection ◽  
Neural Network Models ◽  
Learning Framework ◽  
Different Types

Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use.

scholarly journals Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis

2019 ◽  
Vol 12 (1) ◽  
pp. 86 ◽  
Author(s):  
Rafael Pires de Lima ◽  
Kurt Marfurt
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Network Models ◽  
Remote Sensing Image ◽  
Natural Images ◽  
Natural Image ◽  
Scene Classification ◽  
Neural Network Models

Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification.

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