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

TL;DR

This study develops a CNN-based image quality assessment system for animal cytology, improving disease classification accuracy by reliably identifying low-quality images and artifacts.

Contribution

It introduces a tailored CNN approach using ResNet18 for image quality assessment in animal cytology, analyzing input strategies and artifact detection to enhance diagnosis reliability.

Findings

CNN effectively differentiates valid samples from artifacts

Input size and cropping impact model performance

Image quality assessment improves disease classification accuracy

Abstract

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.