METHOD FOR DETECTING LUMPY SKIN VIRUS (LSV) IN BOVINE ANIMALS

Abstract

ABSTRACT A method (100) for detecting Lumpy Skin Virus (LSV) in bovine animals. Further, the method comprising implementing a Convolutional Neural Network (CNN) model to analyze images of bovine skin. Further, the method (100) comprising the steps of training the CNN model using a dataset comprising images of healthy and LSV-infected bovine skin. Further, the method (100) comprising the steps of processing input images to identify features indicative of LSV. Further, the method (100) comprising the steps of providing a diagnosis based on the analysis of the processed images.

Specification

Description:
FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of veterinary diagnostics and in particular relates to a method for detecting Lumpy Skin Virus (LSV) in bovine animals using machine learning techniques, specifically through the application of Convolutional Neural Networks (CNNs) for image analysis to facilitate rapid and accurate disease identification.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The detection of Lumpy Skin Virus (LSV) is crucial for managing outbreaks in cattle , Claims:1. A method (100) for detecting Lumpy Skin Virus (LSV) in bovine animals, the method comprising the steps of:
implementing a Convolutional Neural Network (CNN) model to analyze images of bovine skin;
training the CNN model using a dataset comprising images of healthy and LSV-infected bovine skin;
processing input images to identify features indicative of LSV; and
providing a diagnosis based on the analysis of the processed images.

2. The method (100) as claimed in claim 1, wherein the CNN model is further enhanced by utilizing data augmentation techniques to increase the diversity of the training dataset, thereby improving the accuracy of the LSV detection.

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