  Cervical cancer is a very common and fatal type of cancer in women.
Cytopathology images are often used to screen for this cancer. Given that there
is a possibility that many errors can occur during manual screening, a
computer-aided diagnosis system based on deep learning has been developed. Deep
learning methods require a fixed dimension of input images, but the dimensions
of clinical medical images are inconsistent. The aspect ratios of the images
suffer while resizing them directly. Clinically, the aspect ratios of cells
inside cytopathological images provide important information for doctors to
diagnose cancer. Therefore, it is difficult to resize directly. However, many
existing studies have resized the images directly and have obtained highly
robust classification results. To determine a reasonable interpretation, we
have conducted a series of comparative experiments. First, the raw data of the
SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets.
Then, the datasets are resized to 224 x 224 pixels. Finally, 22 deep learning
models are used to classify the standard and scaled datasets. The results of
the study indicate that deep learning models are robust to changes in the
aspect ratio of cells in cervical cytopathological images. This conclusion is
also validated via the Herlev dataset.
