Assessing Cardiomegaly in Dogs Using a Simple CNN Model
<|reference_start|>Assessing Cardiomegaly in Dogs Using a Simple CNN Model: 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.<|reference_end|>
@article{deekonda2024assessing,
    title={Assessing Cardiomegaly in Dogs Using a Simple CNN Model},
    author={Nikhil Deekonda},
    journal={arXiv preprint arXiv:2407.06092},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06092},
    primaryClass={cs.CV cs.LG eess.IV}
}