Spike sequences classification for dengue and Zika infections in mosquito neurons using deep pre-trained models

Mosquito-borne diseases are severe hazards to the health of both animals and humans. Aedes aegypti mosquitos are the primary vectors of several medically significant diseases, including dengue and Zika. Therefore, a thorough understanding of the neurons of mosquitos transmitting these diseases can be extremely beneficial in disease prevention. We hope to better comprehend the unique pattern found in considerable values of signal retrieved from mosquito neurons, known as spikes. There is currently no open-source neural spike sequence classification technique for mosquitos. To obtain outstanding outcomes, we demonstrate how to extract and classify spikes from mosquito neuron inputs using transfer learning approaches. Consequently, we highlight the role of deep pre-trained models that were trained using ImageNet weights. The proposed methodology uses electrical spiking activity data from mosquito neurons collected with microelectrode array technology. To assess the method’s performance, data from 0, 1, 2, 3, and 7 days post-infection, reaching more than 15 million samples, were used. In this study, we also look at the influence of days post-infection on recognizing spikes in mosquito neurons. Overall, we attempted for the first time to analyze the distinctive pattern in the spike sequence of mosquito neurons using Artificial Intelligence (AI) approaches and to determine the impact of these spikes over time.