Snake classification as venomous and non-venomous using deep learning algorithm

Snakes are curved, limbless, warm blooded reptiles of the phylum serpents. Any characteristics, including head form, body shape, physical appearance, texture of skin and eye structure, might be used to individually identify nonvenomous and venomous snakes, that are not usual among non-experts peoples. Automated snake image identification is important from different points of view, most importantly, snake bite management. Auto-identification of snake images might help the avoidance of venomous snakes and also providing better treatment for patients. A standard machine learning methodology has also been used to create an automated categorization of species of snake dependent upon the photograph, in which the characteristics must be manually adjusted. As a result, a Convolutional neural network has been proposed in this proposed systems, which classify snakes into two categories: venomous and non-venomous. A set of data of 1766 snake pictures is used to implement CNN model. The dataset is increased with data augmentation technique to enhance the dataset. Experimental results shows that the algorithm has achieved 99.4% accuracy.