DeepCattle: A Deep Learning Framework for Automated Detection and Severity Assessment of Ocular Squamous Cell Carcinoma in Cattle

DOI

10.2991/978-94-6463-858-5_93How to use a DOI?

Keywords

Ocular Squamous Cell Carcinoma; Convolutional Neural Network; Gated Recurrent Unit

Abstract

In this artificial intelligence era, health care plays vigorous role in day today life. The disease caused to the cattle’s are pink eye, new forest disease are caused by infectious Bovine Keratoconjuctivitis (IBK) and cancer eye are caused by Ocular Squamous Cell Carcinoma (OSCC) disease which are often found in young calf where these bacterial diseases spread easily through the transmitter such as flies, etc., The cow suffers more through this disease such as tear straining on the eyes, pains and irritation are exposed due to sunlight. These diseases are not accurately identified through naked eyes where diagnosing at earlier stage will prevent the cattle from the loss of its life. Eyes of the cattle’s are major representation to classify whether the significant cow is infected or not through these disease.Ocular squamous cell carcinoma is a critical ocular disease affecting young calves, which can lead to severe health complications when it is left untreated. The deep learning-based approach for the early detection and classification of OSCC is to identify an early-stage OSCC lesions and assess disease severity. The model leverages a Convolutional Neural Network combined with a Bidirectional Long Short Term Memory to classify ocular images and to determine disease progression. Experimental results demonstrate that the proposed model achieves an accuracy of 93% when applied to high-resolution ocular images, significantly enhancing diagnostic efficiency and supporting timely clinical decision-making.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - D. Manikandan
AU  - S. Saranya
AU  - S. Divya Bairavi
AU  - S. Varadharajan
AU  - S. Dhinesh
PY  - 2025
DA  - 2025/11/04
TI  - DeepCattle: A Deep Learning Framework for Automated Detection and Severity Assessment of Ocular Squamous Cell Carcinoma in Cattle
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1118
EP  - 1127
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_93
DO  - 10.2991/978-94-6463-858-5_93
ID  - Manikandan2025
ER  -