An AI-enabled pre-trained model-based Covid detection model using chest X-ray images

Abstract

The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.

Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

“…Within these some articles develops/propose new methods. For example Elpeltagy M. et al [33] develops a model, Modified ResNet50, where ResNet model was applied with modification of three layers, named, ′Conv′, ′Batch_Normaliz′ and ′Activation_Relu′ for layers which is an accurate, fast, and low-cost auxiliary diagnostic tools to detect Covid-19, where Gupta R.K. et al proposed InceptionResNetV2 model [34]. Some researchers worked through other convenient CNN models like Baz M. et.al.…”

“…Seven of them used only Kaggle dataset to collect their data for training or testing purpose of developed model [35,38,42,44,46,49,51]. Three of them used only GitHub repository which was developed by Dr. Joseph Cohen [33,43,48], other four of them used both of the Kaggle and GitHub database [34,47,50,54]. One used all of the GitHub, Covid-19 radiography database, Kaggle Covid-19 image data collection, and Actual Med Covid-19 Chest X-ray Dataset [36].…”

“…Data Source Data Volume [42] Kaggle 2482 CT scans image and 31 Covid positive along with 10,192 normal X-ray image [48] GitHub 108,948 images [49] Kaggle 3829 X-rays and 3829 X-rays [43] GitHub 535 CT and X-ray images and 485 clinical notes related with them [37] Mendeley Data 17,599 images [44] Kaggle 2481 images [46] Kaggle 2357 CT scan data, 2515 chest X-ray data and 2400 CT and chest X-ray hybrid data [34] Kaggle, GitHub 5856 chest X-ray & CT dataset [54] Kaggle, GitHub 2905 unique images for X-ray 617,775 images from 4154 patients [41] Mendeley Data 8,055 CT scan and 9,544 X-ray images [51] Kaggle 2591 mixed data [33] GitHub 17100 X−ray and CT images [47] Kaggle, GitHub 168 Covid and 168 normal cases for the both Xray and CT scan images [36] GitHub Kaggle and Git Hub 723 X-ray and 3228 CT scans images [38] Kaggle 400 chest X-ray images, and 400 CT scan images [39] Wonkwang University Hospital(WKUH) and Chonnam National University Hospital (CNUH), Italian Society of Medical and Interventional Radiology(SIRM) public database.…”

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

“…Various researchers have utilized pretrained CNN models, such as VGG16, InceptionV3, and ResNet50, to categorize X-ray scans and have shown promising results. 20,21 In another study, 22 the author preprocessed the data before finetuning the extended ResNet50V2 pretrained model, which significantly increased classification accuracy. However, the model required a long training time owing to the large amount of data.…”

“…The lungs were initially segmented from X‐ray images, and the U‐net and VGG19 networks were fine‐tuned to categorize them into positive and negative categories. Various researchers have utilized pretrained CNN models, such as VGG16, InceptionV3, and ResNet50, to categorize X‐ray scans and have shown promising results 20,21 . In another study, 22 the author preprocessed the data before fine‐tuning the extended ResNet50V2 pretrained model, which significantly increased classification accuracy.…”