Epoch in a neural network for brain stroke - Mutiara Simanjuntak; Juanto Simangunsong; Hongjie Dai (Institute of Computer Science (IOCS), 2023)
A neural network is a data processing system consisting of a large number of simple and highly interconnected processing elements in an architecture inspired by the structure of the cortical regions of the brain. Therefore, neural networks can often do things that humans or animals can do, but traditional computers are often lousy. This research discusses brain tumors that can be detected by artificial intelligence. Stroke includes the sudden death of brain cells due to lack of oxygen, blockage of the circulatory system, or severance of flexible pathways to the brain. Therefore the need for action that must be faster to be able to detect this deadly disease. The method used is a Neural Network which can collect knowledge by detecting patterns and relationships between data and learning experiences. So that the detection process is carried out more quickly and the patient can be given medical action as soon as possible. In the study I conducted brain stroke from the number of strokes with a value of 0 4733 and 1 out of 248. This research has a test conducted by conducting epoch training from 1 to 300, the highest score accuracy is in epoch 1 and 2 with more high scores.