Authors: Suresh Babu Nettur, Shanthi Karpurapu, Unnati Nettur, Likhit Sagar Gajja, Sravanthy Myneni, Akhil Dusi, Lalithya Posham
Abstract: Early detection of COVID-19 is crucial for effective treatment and
controlling its spread. This study proposes a novel hybrid deep learning model
for detecting COVID-19 from CT scan images, designed to assist overburdened
medical professionals. Our proposed model leverages the strengths of VGG16,
DenseNet121, and MobileNetV2 to extract features, followed by Principal
Component Analysis (PCA) for dimensionality reduction, after which the features
are stacked and classified using a Support Vector Classifier (SVC). We
conducted comparative analysis between the proposed hybrid model and individual
pre-trained CNN models, using a dataset of 2,108 training images and 373 test
images comprising both COVID-positive and non-COVID images. Our proposed hybrid
model achieved an accuracy of 98.93%, outperforming the individual models in
terms of precision, recall, F1 scores, and ROC curve performance.
Source: http://arxiv.org/abs/2501.17160v1
