Demystifying the Transfer Learning based Detection of Animal Diseases from Images

Abstract

An animal's normal state is altered by sickness which can stop or change critical processes. Concerns over animal diseases have existed as animal lovers interacted with animals and this concern is reflected in the first ideas about religion and magic. Animal illnesses still pose a threat, primarily due to the potential financial costs and risk of human transmission. The study, prevention, and treatment of diseases in animals including wild animals and those utilized in scientific research are the focus of the medical specialty known as veterinary medicine. This research examines recent developments in image-based animal illness detection and predicting the best deep learning model to detect the animal diseases. People now have a better grasp of machine learning and its potential uses in treating animal diseases as a result of the discussion of this paper. Regarding accuracy, DenseNet169 has performed remarkably better than other models whereas ResNet50V2 has least accuracy. These models are trained on the dataset which is built using images collected by the Authors.