Year: 2025
Type: article
Abstract: 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.
Authors: Manikandan Dhayanithi, Dhinesh Sivamani, Saranya Shanmuga Sundaram, SatheaSree Sadasivam, Varadharajan Sampath, Hemavathi Pandala VenkataRao
Institution: Vels University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414025888