Pneumonia detection based on transfer learning and a combination of VGG19 and a CNN Built from scratch

In this paper, to categorize and detect pneumonia from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a combination of the pre-trained model (VGG19) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples. Preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and brightness preserving bi-histogram equalization was also used (BBHE) to improve accuracy. Due to the imbalance of the data set, we adopted some training techniques to improve the learning process of the samples. This network achieved over 99% accuracy due to the proposed architecture that is based on a combination of two models. The pre-trained VGG19 as feature extractor and our designed convolutional neural network (CNN).

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Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

Sensors ◽

10.3390/s21248219 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8219

Author(s):

Amin Ul Haq ◽

Jian Ping Li ◽

Sultan Ahmad ◽

Shakir Khan ◽

Mohammed Ali Alshara ◽

...

Keyword(s):

Transfer Learning ◽

Classification Accuracy ◽

High Performance ◽

Data Augmentation ◽

Early Stage ◽

Accurate Diagnosis ◽

Fine Tuning ◽

Data Set ◽

X Ray ◽

Chest X Ray

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

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Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique

Electronics ◽

10.3390/electronics11010103 ◽

2021 ◽

Vol 11 (1) ◽

pp. 103

Author(s):

Oussama El Gannour ◽

Soufiane Hamida ◽

Bouchaib Cherradi ◽

Mohammed Al-Sarem ◽

Abdelhadi Raihani ◽

...

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Diagnostic Methods ◽

Medical Community ◽

Severe Respiratory Distress ◽

X Ray ◽

General Evaluation ◽

Proposed Model ◽

Chest X Ray

Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%.

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Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

10.21203/rs.3.rs-810768/v1 ◽

2021 ◽

Author(s):

Japman Singh Monga ◽

Yuvraj Singh Champawat ◽

Seema Kharb

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

X Rays ◽

Performance Parameters ◽

Chain Reaction ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

Abstract In the year 2020 world came to a halt due to spread of Covid-19 or SARS-CoV2 which was first identified in Wuhan, China. Since then, it has caused plethora of problems around the globe such as loss of millions of lives, economic instability etc. Less effectiveness of detection through Reverse Transcription Polymerase Chain Reaction and also prolonged time needed for detection through the same calls for a substitute for Covid-19 detection. Hence, in this study, we aim to develop a transfer learning based multi-class classifier using Chest X-Ray images which will classify the X-Ray images in 3 classes (Covid-19, Pneumonia, Normal). Further, the proposed model has been trained with deep learning classifiers namely: DenseNet201, Xception, ResNet50V2, VGG16, VGG-19, InceptionResNetV2 .These are evaluated on the basis of accuracy, precision and recall as performance parameters. It has been observed that DenseNet201 is the best deep learning model with 82.2% accuracy.

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Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp1904-1909 ◽

2022 ◽

Vol 12 (2) ◽

pp. 1904

Author(s):

Farah Flayeh Alkhalid ◽

Abdulhakeem Qusay Albayati ◽

Ahmed Ali Alhammad

Keyword(s):

Data Augmentation ◽

State Of The Art ◽

Virus Disease ◽

Histogram Equalization ◽

Vital Role ◽

X Ray ◽

Current State ◽

Proposed Model ◽

Chest X Ray ◽

Using Data

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

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Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

10.21203/rs.3.rs-810768/v2 ◽

2021 ◽

Author(s):

Japman Singh Monga ◽

Yuvraj Singh Champawat ◽

Seema Kharb

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

X Rays ◽

Performance Parameters ◽

Chain Reaction ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

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Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

Journal of Healthcare Engineering ◽

10.1155/2021/3277988 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Ahmed I. Iskanderani ◽

Ibrahim M. Mehedi ◽

Abdulah Jeza Aljohani ◽

Mohammad Shorfuzzaman ◽

Farzana Akther ◽

...

Keyword(s):

Internet Of Things ◽

Real Time ◽

Transfer Learning ◽

Ensemble Model ◽

Learning Models ◽

X Ray ◽

Proposed Model ◽

Cloud Server ◽

Medical Sensors ◽

Chest X Ray

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

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A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images

Journal of Healthcare Engineering ◽

10.1155/2021/9437538 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Soufiane Hamida ◽

Oussama El Gannour ◽

Bouchaib Cherradi ◽

Abdelhadi Raihani ◽

Hicham Moujahid ◽

...

Keyword(s):

Transfer Learning ◽

Support System ◽

Good Accuracy ◽

Radiographic Images ◽

X Ray ◽

Learning Techniques ◽

Learning Technique ◽

Chest X Ray ◽

The Many ◽

Selection Of

COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.

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