Encephalitis Detection from EEG Fuzzy Density-Based Clustering Model with Multiple Centroid

Encephalitis is a brain inflammation disease. can yield to seizures, motor disability, or some loss of vision hearing. Sometimes, encephalitis be life-threatening and proper diagnosis in an early stage very crucial. Therefore, this paper, we are proposing deep learning model for computerized detection from the electroencephalogram data (EEG). Also, propose Density-Based Clustering classify distinctive waves Encephalitis. Customary clustering models usually employ computed single centroid virtual point define cluster configuration, but does not contain adequate information. To precisely extract accurate inner structural data, multiple centroids approach employed defined which defines configuration by allocating weights each state cluster. The EEG view fuzzy incorporates every enhance model's performance. Also with (FDBC) presented. This employs real clusters using Partitioning Around Centroids algorithm. Experimental results validate medical importance proposed model.