Self-Adaptive and Multi Scale Approach for identifying Tumor using Recurrent Neural Network

Abstract

Brain tumor detection is frequently done using MRI scans. Brain contains several nerve cells and tissues; a tumor occurs when growth of abnormal cells accumulates in region of brain. Early stage of brain tumors is classified into either benign (noncancerous) or malignant (cancerous). To identify tumor in brain comes with it’s challenges, with new technology of improved image screening, it is becoming elementary to detect brain tumor.

This research paper suggests an automated approach where MRI images are used for brain tumor detection. The proposed system initially improves the brain scan by reducing color variations and is known as segmentation is performed on the original image alongside with threshold binary, which is done to segment objects from the background. The method incorporates adaptive mean thresholding, which is essential method to calculate threshold value at any pixel. Also, Otsu’s thresholding is used in the proposed system to perform automatic image thresholding.

 In the method majorly 3 filters are used to facilitate improved segmentation of brain scan image. Kalman filter is one of the most important and widely used estimation algorithms, that produces estimations of hidden variables based on imprecise and uncertain dimensions. Median filter provides result by computing each output sample as median value of the input samples. Gaussian filter here is used to reduce noise and contrast.

This proposed method enables reduction in size and better performance using an architecture known as Xception also reduces computational cost of diagnosis of brain tumor using MRI scans. As the final assessments of the model, we achieve high accuracy and superior performance.