Secured Cloud Application for Detection of Brain Tumor using Deep Learning Algorithms

Brain tumor segmentation is one of the most challenging and essential problems in medical image processing since human-assisted manual classification can lead to inaccurate prognosis and diagnosis. Having a huge amount of data to analyse makes the problem exponentially more complex. As a result of the great variety of tumor appearances and the close resemblance between normal and tumor tissues, it is difficult to isolate tumor regions from images. By using convolutional neural networks, this study was able to extract 2D MR brain pictures and identify tumors in the brain (MRI). A real-time dataset with a wide range of tumor sizes, locations, shapes, and intensities is used for performing system analysis. A Convolutional NeuralNetwork (CNN) built with Keras and Tensor Flow is used in this research work since it outperforms the ordinary neural networks. An online cloud application is used to determine whether an image contains a tumor or not, and it employs the secure AES256 approach for transmitting an MRI image to the cloud server. According to the obtained results, CNN produces an accuracy rate of 97.87%. The primary goal of this study is to differentiate the normal and abnormal pixels in order to determine whether or not the patient is at risk of developing a tumor.