Deep Learning Based Early Detection of Ocular Squamous Cell Carcinoma in Calves

Ocular squamous cell carcinoma (OSCC) is a prevalent and aggressive ocular disease in cattle that can cause
severe health complications, reduced productivity, and economic losses if left untreated. Traditional
diagnostic methods are often time-consuming and reliant on expert veterinary evaluation, which can delay
timely intervention. The study proposes a deep learning-based approach for the early detection and
classification of OSCC in young calves using convolutional neural networks (CNNs). High-resolution ocular
images were used to train a CNN model capable of identifying early-stage lesions and classifying disease
severity with high accuracy. The system leverages automated feature extraction to distinguish between healthy
and diseased tissues, thereby reducing the dependency on manual image interpretation. Experimental results
demonstrate the potential of the proposed method to provide 95% of accuracy with efficient, accurate, and
scalable diagnostic tool that assists veterinarians in making prompt, evidence-based treatment decisions,
where this ultimately improves animal welfare and farm productivity.
Keywords: Convolutional Neural Network, Ocular Image, Deep Learning, Young Calves, Veterinary.