1Research Scholar, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, Andhra Pradesh, India
2Professor, Department of CSE, Koneru Lakshmaiah Education Foundation (KLEF), Guntur, Andhra Pradesh, India
The latesttechnological developments in medicinal data need accurate diagnosis which may demand corrective prediction using advanced machine learning algorithms. Brain tumor analysis is an emerging field in healthcare domain as lots of death occurs due to inaccurate and late detection of this disease. This paper focuses on real time detection of tumor levelsin three dimensional MRI images.3D MRI image in DICOM format is converted into JPEG format then denoising, grey scale and blurring techniques were applied on it to remove noise. Feature extraction algorithms were applied on each layer of 3D image to generate feature vectors and save these images on HDFS. Clustering techniques is used to identify region of interest in each and every layerof 3D image. Then classification algorithm is used on learning trained datasets to predict accurate level of brain tumor. To speed up diagnosis time and improve efficiency through parallel processing Hadoop's MapReduce framework is used.
Medical image processing, MRI images, feature extraction, clustering, Kmeans, classification, Map Reduce, HDFS
