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

被引:16
作者
Srinivas, K. [1 ]
Sri, R. Gagana [1 ]
Pravallika, K. [2 ]
Nishitha, K. [1 ]
Polamuri, Subba Rao [3 ]
机构
[1] VR Siddhartha Engn Coll, Dept CSE, Vijayawada 520007, India
[2] Sir CR Reddy Engn Coll, Dept CSE, Eluru 534007, India
[3] Bonam Venkata Chalamayya Engn Coll Autonomous, Dept CSE, Odalarevu 533210, India
关键词
Corona virus; COVID-19; Inception V3; VGG16; IV3-VGG; RT-PCR; ResNet50; DenseNet121; MobileNet; Chest X-ray; SARS-COV-2; DIAGNOSIS; ENSEMBLE;
D O I
10.1007/s11042-023-15903-y
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:36665 / 36682
页数:18
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