A Snake classification model for snakebite envenoming management

Mabinda, Mariam
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Strathmore University
Snakebite envenoming is a potentially life-threatening disease caused by the injection of toxins through a bite or venom sprayed into the victim’s eyes by certain venomous snake species. WHO program dubbed Neglected Tropical Disease Program (NTD) of 2019 indicated that about 5.4 million snake bites occur each year, resulting in 1.8 to 2.7 million cases of envenoming. Of this about 81,000–138,000 deaths occur and approximately 400,000 people are permanently disabled annually. Kenya is approximated to have more than 15,000 bites annually. Correct identification of the snake species in question plays a critical role in the proper administration of the right first aid and suitable prescription of the anti-venom for the patient. Currently, there is no automated method of identifying snake species using images in Kenya. The usual practice is to, kill the snake and carry it along with the patient to the hospital or to give a visual feature description of the biting snake. Also, a blood test can be done to look for the presence of toxins associated with the described snake species. The challenge however is, that the time required for test results to be out can jeopardize patients' survival depending on the type of venom injected. Furthermore, the cost associated with the test is also punitive. The ability to correctly classify a snake species is a challenging task for both humans and machines mainly because of subtle differences between different snake species and strong variety within the same species. Existing studies used a combination of feature extraction methods and deep neural networks and yielded an accuracy of 90%. These models applied Principal component analysis (PCA) and Linear discriminant analysis (LDA) as feature extractors. However, the use of the Singular Value Decomposition (SVD) algorithm was not explored despite its apparent advantage. This research study solved the classification challenge by creating a Kenyan snake species dataset and developing a snake species classification model that predicts a snake species based on the image and classifies it according to its venom toxicity. The study carried out feature reduction of the images using the SVD algorithm and passed these features as input to a deep learning model using transfer learning. The model was trained using 4521 labelled snake images via supervised transfer learning using MobileNetV2. The model was trained, validated, tested, and achieved an outstanding classification accuracy of 96 %. The model surpassed the accuracy of the existing model.
Submitted in partial fulfillment of the requirements for the Degree of Masters of Science in Information Technology at Strathmore University.