  A method of a Convolutional Neural Networks (CNN) for image classification
with image preprocessing and hyperparameters tuning was proposed. The method
aims at increasing the predictive performance for COVID-19 diagnosis while more
complex model architecture. Firstly, the CNN model includes four similar
convolutional layers followed by a flattening and two dense layers. This work
proposes a less complex solution based on simply classifying 2D-slices of
Computed Tomography scans. Despite the simplicity in architecture, the proposed
CNN model showed improved quantitative results exceeding state-of-the-art when
predicting slice cases. The results were achieved on the annotated CT slices of
the COV-19-CT-DB dataset. Secondly, the original dataset was processed via
anatomy-relevant masking of slice, removing none-representative slices from the
CT volume, and hyperparameters tuning. For slice processing, a fixed-sized
rectangular area was used for cropping an anatomy-relevant region-of-interest
in the images, and a threshold based on the number of white pixels in binarized
slices was employed to remove none-representative slices from the 3D-CT scans.
The CNN model with a learning rate schedule and an exponential decay and slice
flipping techniques was deployed on the processed slices. The proposed method
was used to make predictions on the 2D slices and for final diagnosis at
patient level, majority voting was applied on the slices of each CT scan to
take the diagnosis. The macro F1 score of the proposed method well-exceeded the
baseline approach and other alternatives on the validation set as well as on a
test partition of previously unseen images from COV-19CT-DB dataset.
