scholarly journals A Transfer Learning Method for Pneumonia Classification and Visualization

2020 ◽  
Vol 10 (8) ◽  
pp. 2908 ◽  
Author(s):  
Juan Luján-García ◽  
Cornelio Yáñez-Márquez ◽  
Yenny Villuendas-Rey ◽  
Oscar Camacho-Nieto
Keyword(s):  
Transfer Learning ◽  
Causes Of Death ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Learning Method ◽  
X Ray ◽  
Chest X Ray ◽  
Aided Diagnosis ◽  
Positive Results

Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

scholarly journals Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5813
Author(s):  
Muhammad Umair ◽  
Muhammad Shahbaz Khan ◽  
Fawad Ahmed ◽  
Fatmah Baothman ◽  
Fehaid Alqahtani ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Fine Tuning ◽  
Feature Maps ◽  
Virus Family ◽  
X Ray ◽  
Normal Chest ◽  
Chest X Ray ◽  
Confusion Matrices

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

The Classification of Covid-19 cases through the Employment of Transfer Learning on X-ray images

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 44-51
Author(s):  
Nur Ameerah Hakimi ◽  
Mohd Azhar Mohd Razman ◽  
Anwar P. P. Abdul Majeed
Keyword(s):  
Transfer Learning ◽  
Screening Method ◽  
X Ray ◽  
The People ◽  
Machine Learning Model ◽  
The World ◽  
Chest X Ray ◽  
Alternative Test ◽  
Chest Radiology

Covid-19 is a contagious disease that known to cause respirotary infection in humans. Almost 219 countries are effected by the outbreak of the latest coronavirus pandemic, exceed 100 millions of confirmed cases and about 2 million death recorded aound the world. This condition is alarming as some of the people who are infected with the virus show no symptoms of the disease. Due to the number of confirmed cases rapidly rising around the world, it is crucial  find another method to diagnose the disease at the beginnings stage in order to control the spreading of the virus. Another alternative test from the main screening method is by using chest radiology image based detection which are X-ray or CT scan images. The aim of this research is to classify the Covid-19 cases by using the image classification technique.The dataset consist of 2000 images of chest X-ray images and have two classes which are Covid and Non-Covid. Each of the class consists of 1000 images.This research compare the performance of the various Transfer Learning models (VGG-16, VGG-19, and Inception V3) in extracting the feature from X-ray image combined with machine learning model (SVM, kNN, and Random Forest) as a classifier. The experiment result showed the VGG-19, VGG-16, and Inception V3 coupled with optimized SVM pipelines are comparably efficient in classifying the cases as compared to other pipelines evaluated in this reaseach and could archieved 99% acuuracy on the test datasets.

scholarly journals Effective Survey on Detection and Classification of COVID-19 Suspected Individual Using CT scan Images

2021 ◽  
pp. 294-299
Author(s):  
Snehal R. Sambhe ◽  
Dr. Kamlesh A. Waghmare
Keyword(s):  
Ct Scan ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Feature Extractor ◽  
Chest X Ray ◽  
Coronavirus Infection ◽  
Aided Diagnosis ◽  
Excessive Risk

As insufficient testing kits are available, the development of new testing kits for detecting COVID remains an open vicinity of research. It’s impossible to test each and every patient suffering from coronavirus symptoms using the traditional method i.e. RT-PCR. This test requires more time to produce results and have less sensitivity. Detecting feasible coronavirus infection using chest X-Ray may also assist quarantine excessive risk sufferers while testing results are disclosed. A learning model can be built based on CT scan images or Chest X-rays of individuals with higher accuracy. This paper represents a computer-aided diagnosis of COVID 19 infection bases on a feature extractor by using CNN models.

scholarly journals A deep learning method for classification of chest X-ray images

2021 ◽  
Vol 1848 (1) ◽  
pp. 012030
Author(s):  
Jiashi Zhao ◽  
Mengmeng Li ◽  
Weili Shi ◽  
Yu Miao ◽  
Zhengang Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Method ◽  
X Ray ◽  
Chest X Ray

scholarly journals Problems of Deploying CNN Transfer Learning to Detect COVID-19 from Chest X-rays

2020 ◽  
Author(s):  
Taban Majeed ◽  
Rasber Rashid ◽  
Dashti Ali ◽  
Aras Asaad
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Diagnostic Process ◽  
X Rays ◽  
X Ray ◽  
Support Mechanism ◽  
Speed Up ◽  
Chest X Ray ◽  
The Many

AbstractThe Covid-19 first occurs in Wuhan, China in December 2019. After that the virus spread all around the world and at the time of writing this paper the total number of confirmed cases are above 4.7 million with over 315000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To achieve this task, we first use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images fed into CNN models without any preprocessing to follow the many of researches using chest X-rays in this manner. Next, a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed most to the decision of CNNs back to the original image to visualize the most discriminating regions on the input image.We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect, and approve, the region(s) of the input image used by CNNs that lead to its prediction.

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