Assessing Cardiomegaly in Dogs Using a Simple CNN Model
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910518089875456 |
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| author | Deekonda, Nikhil |
| author_facet | Deekonda, Nikhil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing Cardiomegaly in Dogs Using a Simple CNN Model Deekonda, Nikhil Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | Assessing Cardiomegaly in Dogs Using a Simple CNN Model |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2407.06092 |