An Optimized Deep Learning Framework For Brain Tumor Detection And Classification Using Image Processing in Medical Health Society
Keywords:
Brain Tumor Detection, Magnetic Resonance Imaging (MRI), Deep Learning, Convolutional Neural Network (CNN), VGG16, Transfer Learning, Medical Image Processing, Automated Tumor Detection, Binary Classification, Neural Networks, Feature Extraction, Computer-Aided Diagnosis (CAD), Healthcare Analytics, Tumor Segmentation, Image Classification, Early Tumor Diagnosis, Clinical Decision Support System, Machine Learning in Healthcare, Digital Medical Imaging, Artificial Intelligence in Radiology.Abstract
Now a day’s tumor is second leading cause of cancer. Due to cancer large no of patients are in danger. The medical field needs fast, automated, efficient and reliable technique to detect tumor like brain tumor. Detection plays very important role in treatment. If proper detection of tumor is possible then doctors keep a patient out of danger. Various image processing techniques are used in this application. Using this application doctors provide proper treatment and save a number of tumor patients. A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in a way that they eventually take up all the nutrients meant for the healthy cells and tissues, which results in brain failure. Currently, doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. A tumor is a mass of tissue it grows out of control. We can use a Deep Learning architectures CNN (Convolution Neural Network) generally known as NN (Neural Network) and VGG 16(visual geometry group) Transfer learning for detect the brain tumor. The performance of model is to predict image tumor is present or not in image. If the tumor present it returns yes otherwise returns no.
