COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images

Abstract

The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription–Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called “Inception V3 with VGG16 (Visual Geometry Group)” is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet.

Schlieren imaging and video classification of alphabet pronunciations: exploiting phonetic flows for speech recognition and speech therapy

“…Ablation experiments to evaluate the contributions of individual components to the system’s performance, or comparative experiments with other models, were not performed [ 54 ]. Selecting the most appropriate CNN for this task necessitates exploring and evaluating more contemporary methods, including models with more sophisticated and deeper architectures, such as ResNet50 [ 55 ] and VGG19 [ 56 ], known for their advanced features, and alternatives like MobileNet [ 57 ] and EfficientNet [ 58 ], known for their simpler structures and greater time efficiency. Each approach offers unique benefits and trade-offs, warranting consideration for optimal performance in this task.…”

Development of a chest X-ray machine learning convolutional neural network model on a budget and using artificial intelligence explainability techniques to analyze patterns of machine learning inference

“… 8 , 9 Other model architectures such as Inception V3, VGG16, and even ResNet have been employed in the past. 10 , 11 …”

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

“…Four convolutional neural network (CNN) models were selected for this study: AlexNet, ResNet-50, MobileNet, and VGG-19. AlexNet and ResNet-50 were selected because they were the 2012 and 2015 winners of the ImageNet competition, respectively [32,33]. AlexNet was groundbreaking in its use of GPUs for training deep neural networks, while ResNet-50 introduced residual connections between different layers to improve gradient flow and enable the training of even deeper neural networks [34,35].…”

“…Considering that such variability was reflective of the inherent advantages and setbacks of CNN models, hybrid or ensemble models leveraging multiple networks have been explored. By combining Inception V3 with VGG16, Srinivas et al [33] achieved a 98% accuracy of COVID-19 prediction using 243 X-ray images, which outperformed Inception V3, VGG16, ResNet-50, DenseNet121, and MobileNet when tested individually. Similarly, Wang et al [34] integrated features extracted from Xception, MobileNetV2, and NasNetMobile and made the classification via a confidence fusion method.…”