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.
@article{arxiv.2308.06055,
title = {Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification},
author = {Jan Krupiński and Maciej Wielgosz and Szymon Mazurek and Krystian Strzałka and Paweł Russek and Jakub Caputa and Daria Łukasik and Jakub Grzeszczyk and Michał Karwatowski and Rafał Fraczek and Ernest Jamro and Marcin Pietroń and Sebastian Koryciak and Agnieszka Dąbrowska-Boruch and Kazimierz Wiatr},
journal= {arXiv preprint arXiv:2308.06055},
year = {2023}
}