Parasitical Disease Prediction Model – a Deep Learning Based Approach

Abstract

Female anopheles mosquitoes transmit the highly contagious parasitical diseases. Animals as well as humans are harmed by this sickness. In the worst-case scenario, this illness could result in the patient's death if it is not adequately diagnosed in the early stages. It is exceedingly difficultly in confirming 0 the presence of ailment in industry owing towards a deficiency of exceedingly methodological competence. Cutting-edge this situation, data retrieval assistance is required for accurate and quick disease identification. With the aid of IT division buzzword know-hows like Machine Learning, Deep Learning, and Non-natural Acumen, modern IT sectors are working tirelessly to combat this sickness. If appropriately applied, these technologies will continue to be the backbone of healthcare as they have been in recent years. In order to determine if an organism is infected with a parasite or not, this study applies the Convolutional Neural Network (CNN) algorithm to a minuscule carbon copy of the contaminated blood cells. 15 out of 16 random photos can be accurately predicted by our suggested model, which achieved an accuracy of 95.23 percent.