Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality
  Assessment for Accurate Disease Classification
Jan Krupiński Maciej Wielgosz Szymon Mazurek Krystian Strzałka Paweł Russek Jakub Caputa Daria Łukasik Jakub Grzeszczyk Michał Karwatowski Rafał Fraczek Ernest Jamro Marcin Pietroń Sebastian Koryciak Agnieszka Dąbrowska-Boruch Kazimierz Wiatr
This paper presents a computer-aided cytology diagnosis system designed for
animals, focusing on image quality assessment (IQA) using Convolutional Neural
Networks (CNNs). The system's building blocks are tailored to seamlessly
integrate IQA, ensuring reliable performance in disease classification. We
extensively investigate the CNN's ability to handle various image variations
and scenarios, analyzing the impact on detecting low-quality input data.
Additionally, the network's capacity to differentiate valid cellular samples
from those with artifacts is evaluated. Our study employs a ResNet18 network
architecture and explores the effects of input sizes and cropping strategies on
model performance. The research sheds light on the significance of CNN-based
IQA in computer-aided cytology diagnosis for animals, enhancing the accuracy of
disease classification.