Search

Now days, the human being are affecting by so many diseases in all over the world. If we will consider about the tumor in the encephalon, then detection of tumor often involves radiological imaging. Radiological Imaging is used to check the spread of tumor and progress of treatment and also used to monitor cancer. The manual detection of the Encephalon tumor becomes a meticulous and chaotic task for the clinical experts. If the doctors or various departmental specialists want to eliminate the spoiled cancer areas from Magnetic Resonance Images, then it becomes very hectic and takes more time. The main aim of this work is to detect the encephalon tumor regions. Once the brain is going to be scan then it offers proficient and quick identification of encephalon tumor. To modernize the performance and abbreviate the intricacy involved in the image detection process in the proposed system as well as the Fuzzy CMean predicated image segmentation processes are used. Furthermore, to expand the variance of different images, particularly when the data are worn in image is signified beside different variance of images and then another technique has been implemented in the paper that is Histogram Equalization. The proposed method is completely automatic and tested on different kinds of brain MR images and proved as robust.

Nowadays, so many people are living in world. If so many people are living, then the diseases are also increasing day by day due to adulterated and chemical content food. The people may suffer either from a small disease such as cold and cough or from a big disease such as cancer. In this work, we have discussed on the encephalon tumor or cancer which is a big problem nowadays. If we will consider about the whole world, then there are deficiency of clinical experts or doctors as compared to the encephalon tumor affected person. So, here, we have used an automatic classification of tumor by the help of particle swarm optimization (PSO)-based extreme learning machine (ELM) technique with the segmentation process by the help of improved fast and robust fuzzy C mean (IFRFCM) algorithm and most commonly feature reduction method used gray level co-occurrence matrix (GLCM) that may helpful to the clinical experts. Here, we have used the BraTs (“Multimodal Brain Tumor Segmentation Challenge 2020”) dataset for both the training and testing purpose. It has been monitored that our system has given better classification accuracy as an approximation of 99.47% which can be observed as a good outcome.