Innovation in Stroke Identification Using Machine Learning Based Approach Using Neuro images

A single stroke can lead to lasting harm or even loss of life across the globe; catching it fast improves chances. This study introduces a digital method meant to spot early signs through brain CT scans. Key visual traits are picked out by an algorithm shaped via genetic optimization and processed using a CNN setup. These patterns then move into a Bidirectional LSTM network for sorting and identification. Instead of relying on one test, multiple checks plus cross-check steps help judge how well the system works. The new method hits 96.5% accuracy, beating older machine learning and deep learning approaches. Doctors could use it to spot strokes faster - making prevention easier along the way.