Salp Swarm Algorithm with Multilevel Thresholding Based Brain Tumor Segmentation Model

Biomedical image processing acts as an essential part of several medical applications in supporting computer aided disease diagnosis. Magnetic Resonance Image (MRI) is a commonly utilized imaging tool used to save glioma for clinical examination. segmentation plays vital role healthcare decision making process which also helps identify the affected regions MRI. Though numerous models are available literature, it still needed develop effective BT. This study develops salp swarm algorithm with multi-level thresholding based brain tumor (SSAMLT-BTS) model. The presented SSAMLT-BTS model initially employs bilateral filtering on noise removal and skull stripping pre-processing phase. In addition, Otsu approach applied segment biomedical images optimum threshold values chosen by use SSA. Finally, active contour (AC) technique suspicious image. A comprehensive experimental analysis performed using benchmark dataset outcomes inspected many aspects. simulation reported improved over recent approaches maximum accuracy 95.95%.