Assessing Cardiomegaly in Dogs Using a Simple CNN Model

TL;DR

This study presents DogHeart, a new dataset and a simple CNN model that classifies canine cardiomegaly severity with 72% accuracy, aiding early veterinary diagnosis.

Contribution

Introduces a novel dataset and a straightforward CNN architecture for automated cardiac condition assessment in dogs.

Findings

72% classification accuracy achieved

Dataset includes 2000 images across categories

Potential for early veterinary intervention

Abstract

This paper introduces DogHeart, a dataset comprising 1400 training, 200 validation, and 400 test images categorized as small, normal, and large based on VHS score. A custom CNN model is developed, featuring a straightforward architecture with 4 convolutional layers and 4 fully connected layers. Despite the absence of data augmentation, the model achieves a 72\% accuracy in classifying cardiomegaly severity. The study contributes to automated assessment of cardiac conditions in dogs, highlighting the potential for early detection and intervention in veterinary care.