Open Access Open Access  Restricted Access Subscription Access

SpeciSphere Classification: An app that classifies a image and provides veterinary diagnostics for canines (Dogs)

A Hemanth Datta, A Hrushi Jiyyan, Dr. K. Sreekala, Mr. G. Nagi Reddy, Dr.A. Nagesh

Abstract


The paper discusses an intelligent image diagnosis and classification system that can identify general objects and make specific disease prediction in dogs. It starts by classifying the webcam-captured or uploaded image with a trained ResNet-50 convolutional neural network. Once it identifies the presence of a dog, it goes ahead to detect the breed and enables a rule-based diagnostic module to predict probable diseases, focusing on breed-related conditions. The diagnostic engine is based on an organized database correlating symptoms and breed predispositions to prevalent dog illnesses, facilitating more precise and context-dependent diagnosis. The web app, implemented with Python

and Streamlit, supports real-time webcam functionality, user login, and safe activity logging through SQLite. The modular design of the system ensures user-friendliness, flexibility, and realistic applicability for pet owners, vets, and scientists. Possible enhancements in the future can involve integrating image-based disease detection and explainable AI methods to enable more automated and scalable diagnoses.


Full Text:

PDF

References


J. Li, Y. Wang, and Z. Liu, “Multi-Scale Vision Transformers for Image Classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.

M. D’Souza and A. Verma, “Confidence-Based Interpretability in Neural Network Predictions,” Journal of Applied AI Research, 2025.

B. Zhang et al., “Modulated Convolutional Networks,” IEEE Transactions on Neural Networks and Learning Systems, 2025.

T. Singh and N. Aggarwal, “Voice-Enabled Interfaces for Healthcare Diagnostics,” Journal of Smart Health Technologies, 2024.

Z. Chen, X.-J. Wu, T. Xu, and J. Kittler, “Discriminative Dictionary Pair Learning With Scale-Constrained Structured Representation for Image Classification,” IEEE Trans. Neural Netw. Learn. Syst., 2023.

R. Chen and H. Luo, “Symptom-Guided Diagnostic Systems Using Hybrid AI Models,” Journal of Medical Informatics and Intelligent Systems, 2022.

K. Tang et al., “Decision Fusion Networks for Image Classification,” IEEE Transactions on Neural Networks and Learning Systems, 2022.

Y. Hong et al., “ResDNet: Efficient Dense Multi-Scale Representations With Residual Learning for High-Level Vision Tasks,” IEEE Transactions on Neural Networks and Learning Systems, 2022.


Refbacks

  • There are currently no refbacks.