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\begin{document}

\title{Convolutional Neural Network (CNN) inCOVID-19 Detection: A Case Study
with Chest CT Scan Images}



\author[1,1]{Khandaker Nusaiba Hafiz }%
\author[2]{Khandaker Foysal Haque }%
\affil[1]{University of California}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
Deep Learning, especially Convolutional Neural Networks (CNN) have been
performing very well for the last decade in medical image
classification. CNN has already shown a great prospect in detecting
COVID-19 from chest X-ray images. However, due to its three dimensional
data, chest CT scan images can provide better understanding of the
affected area through segmentation in comparison to the chest X-ray
images. But the chest CT scan images have not been explored enough to
achieve sufficiently good results in comparison to the X-ray images.
However, with proper image pre-processing, fine tuning and optimization
of the models better results can be achieved. This work aims in
contributing to filling this void of the literature. On this aspect,
this work explores and design both custom CNN model and three other
models based on transfer learning: InceptionV3, ResNet50 and VGG19. The
best performing model is VGG19 with an accuracy of 98.39\% and F-1 score
of 98.52\%. The main contribution of this work includes: (i) modeling a
custom CNN model and three pre-trained models based on InceptionV3,
ResNet50, and VGG19 (ii) training and validating the models with a
comparatively larger dataset of 1252 COVID19 and 1230 non-COVID CT
images (iii) fine tune and optimize the designed models based on the
parameters like number of dense layers, optimizer, learning rate, batch
size, decay rate, and activation functions to achieve better results
than the most of the state-of-the-art literature (iv) the designed
models are made public in {[}1{]} for reproducibility by the research
community for further developments and improvements.%
\end{abstract}%



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