An Automated Framework for Brain Tumour Class Detection

With the significant growth in medical imaging techniques and the demand for better processing of medical information, the mandate of automation in disease detection is also increasing. In the modern time, the nature of the diseases has also changed. The highly mortal diseases are becoming difficult to detect due to the high involvements of medical individual and high dependency of human knowledges. The human knowledge is prone to error and often criticized for longer time delay for processing information in disease detections. Thus, the demand from the modern computing and implementation based computational algorithms are to automate the medical disease detection processes with greater accuracy. One such disease with superior mortal rate is brain tumours or cancerous growth in the brain tissues. The regular medical practice approaches have demonstrated the challenges in detection of the tumours and more so the nature of the tumours. Ill detection of the tumour type or the shape of the tumour or the size of the tumours can lead to life threats. Thus, the need for automation in detection is the most expected form of replacements in place of manual diagnosis. Another challenge is the available data formats for such disease reports. The available reports for brain tumour are only in the form of magnetic resonance images or MR Images. The MR Images can cause higher obstacles for further processing as due to the capture process of the patient data. Often, it is observed that the noise present in the MR images makes the processing vulnerable in accuracy. A number of parallel research outcomes have demonstrated significant outcomes of detection of available tumours in the human brain using segmentation methods. Nonetheless, all parallel attempts are criticized for not able to model the growth or the nature of the tumours presents in the human brain. Thus, this work proposes a novel automated framework for detection of tumour types by deploying progressive segmentation and model the growth stages based on features. The parallel outcomes have outrun on detection accuracy due to the use of standard segmentation methods, which is designed for generic image processing and bound not to match the specificity of medical image processing. Thus, this work introduces a novel segmentation method, which is progressive in nature for higher accuracy. This work also outcomes into an automated feature extraction model for brain tumours. The major contribution of the work is to determine the nature of tumour and a sustainable prediction model for tumour stages inside the human brain. The work demonstrates high accuracy for correct detection and prediction of the patient’s life threats in in real time order to take timely medication for making the precious human life more precious.