Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification

11 Aug 2023 · 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.

PDF Abstract