Brain tumor detection and classification using ML

A Brain tumor is considered as a one of the aggressive diseases among the childrens and adults. Brain tumor accounts for 85 to 90 percent of all primary Central Nervous Systems ( CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and 36 percent for women. Brain tumors are classified as : Benign Tumor, Malignant Tumor, Pituitary Tumor, etc.Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of data is generated through the scans. These images are examined by the level of complexities involved in brain tumors and their properties. Application of automated classification techniques using Machine Learning(ML) and Artificial Intelligence(AL) has consistently shown higher accuracy than Convolution-Neural Network(CNN),Artificial Neural Network(ANN), and transfer-Learning(TL) would be helpful to doctors all around the world. Brain tumor are complex. There are a lot of abnormalities in the sizes and location of the brain tumor. This makes it really difficult for complete understanding of the nature of the tumor. Also a professional Neurosurgeon is required for MRI analysis. Often time’s in developing countries the lack of skillful doctors and lack of knowledge about tumors makes it really challenging and time- consuming to generate reports from MRI. So an automated system on Cloud can solve this problem. To detect and Classify Brain Tumor using, CNN or ANN; as an asset of Deep Learning and to examine the tumor position (segmentation). The dataset contains 3 folders: Brain_Glioma, Brain_Maningioma and Brain_Tumor which contains 1500 Brain CT Images.