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Architecture-based Evaluation of VGG16 and ResNet Models for an Online Deep Learning Environment for Medical Applications
3rd International Conference on Innovations in Science and Technology for Sustainable Development, ICISTSD 2022 ; : 62-67, 2022.
Article in English | Scopus | ID: covidwho-2228891
ABSTRACT
Image classification using deep learning models has evolved impressively well in the past decade. Datasets containing millions of images grouped into thousands of classes have been used to train and test these models. Medical image classification however still faces the challenge of scarcity in datasets. Gathering data from various locations and placing it in a commonly accessed dataset is highly time-consuming. Diseases need real-Time response just like any other mission-critical operation and online deep learning could be handy. There are many pre-Trained models which acquired good accuracy on large datasets. But as the depth of the model increases the time taken to train the model and the number of computations also increase. In this paper, we evaluated two models with different architectures. VGG16 is a 16-layer normal stack of convolutional layers and ResNet50V2 is a stack of residual blocks with skip connections and 50 layers. We used a Computer Tomography (CT) Lung image dataset to classify images into COVID, healthy and pneumonia images. We found that VGG16 is taking lesser time and computations with reduced loss when compared to the ResNet50V2 model. We finally conclude that ResNet50V2 is taking more time to train images as the model is 50 layers deep, whereas the VGG16 model is only 16 layers deep. Also, images that show mild infection were predicted as healthy images by ResNet50V2 but predicted correctly by the VGG16 model. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: 3rd International Conference on Innovations in Science and Technology for Sustainable Development, ICISTSD 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: 3rd International Conference on Innovations in Science and Technology for Sustainable Development, ICISTSD 2022 Year: 2022 Document Type: Article