A Novel Deep Learning and Image Processing Based Technique for Lumpy Skin Disease Detection

Lumpy skin disease is a very infectious disease in cattle. Every year many cattle die because of lumpy skin disease. Recently, Indian states especially Rajasthan, Haryana, Punjab have witnessed the death of many cows due to lumpy skin infection. This disease becomes very dangerous and spreads at a high rate if not treated well at the time of infection. A proper technique is required to detect the lumpy skin infection in cattle so that proper diagnosis can be adopted to stop the infection from spreading and to save the life of the infected cattle. With the emergence of machine learning and deep learning approaches it seems that detection can be done at the inception of infection in the cattle. Many researchers have suggested various ways to detect the lumpy skin disease but there is need of an approach which can guarantee the high accuracy, precision, recall and f1 score for the lumpy skin infection. We have used some of the advanced pre-trained deep learning models and also proposed a modified convolution neural network model for lumpy skin infection detection. We collected dataset from the online platform. We collected total 936 cattle images including healthy and lumpy skin infected cattle. Out of 936 cattle images 421 were lumpy affected and rest 515 were images of the healthy cattle. We split the image in training and testing dataset. We used 80 percent of images for training the models and 20 % of images for testing the performance of the models. We trained InceptionV3, VGG16, ResNet50V2 and modified CNN model under similar condition by properly tuning the hyper parameters. We have observed that proposed modified CNN model outperformed all the reset three models. On training data, modified CNN yield an accuracy of 99.01 %, InceptionV3 furnished an accuracy of 98.85 %, Resnet50V2 has an accuracy of 92.09% and VGG16 guaranteed an accuracy of 92%. On testing data, proposed modified CNN, InceptionV3, ResNet50V2 and VGG 16 has accuracy 97.87 %, 97.34%, 93.09% and 93.09% respectively.