Analysis Of Brain Tumor Segmentation using Convolutional Neural Network with Magnetic Resonance Imaging

Computer vision plays a vital role in human health care area. The various applications of these techniques have been playing a key role in reducing physicians judgment in diagnosis. Especially, in the case of brain related cases, it is suggested and required to take utmost care and a small error in diagnosis leads to disastrous. Although various methods like X ray, ct scan are available, mri is more reliable and danger free. High contrast images of brain tissues can be taken by using mri technique. Brain Tumor segmentation involves separation of tumors from normal brain tissues. The segmentation process helps in fetching required information for the purpose of diagnosis and what treatment has to be taken. It plays a vital role in medical image processing. The process involves risk because of abnormal form and irregularities at the boundaries. Determination of these tumors at the earlier stage helps to give better treatment to the patients. Usually, Physicians deal with the affected people by performing manual way of segmentation which results in the absence of time management. To overcome this manual segmentation problem, we developed CNN method of segmentation procedure which performs well by taking various factors like accuracy, recall and precision into consideration. MRI is one of the conservative techniques for prior diagnostic tool of brain tumor. The necessary measures for diagnosis and treatment planning have to be taken place quickly if the tumor is malignant in order to enhance successful treatment. By applying the following method, the doctors are capable of making accurate decision and required treatment can be done in an effective manner. The computer aided diagnostic system, MRI helps in reduction of workload by doctors.