Online ISSN: 2515-8260

Keywords : FCM


An Efficient Brain Tumor Classification And Detection Using Evolutionary Approach For Healthcare System

Vinoth Kumar. V; Dr. Paluchamy. B

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 658-670

The growth of irregular cells within the brain region with the prearrangement of tissues is characterized as a Brain Tumor that leads death to people. Comparing to other categories of cancers, a brain tumor is the most deadly disease that has to be detected and treated in the previous stage. Due to cells' complex formation, the tumor detection process is complicated with simple image processing methodologies. Moreover, for providing proper and efficient treatment to the patients, an exact cancer segmentation and classification technique must process the input of brain images as Magnetic Resonance Imaging (MRI) scans. Based on that, this paper develops a novel approach called Soft Computing based Brain Tumor Detection and Classification (SC-BTDC) with the obtained MRI. In the present scenario of tumor detection from image processing, soft computing techniques play a significant role. Hence, it is adopted in this work. The method contains phases such as pre-processing, Fuzzy c-Means clustering-based segmentation, feature extraction, and image classification. The median pre-processing filter and edge detection methods are incorporated for noise removal and clearly define the image in the stage of the median pre-processing filter. Further, FCM based clustering is performed for the image segmentation process, following, factors of Gray Level Co-Occurrence Matrix (GLCM) found feature extraction is established. The final phase includes the classification process using the soft computing technique called Artificial Neural Network (ANN) classification. The proposed system acquires a higher accuracy rate and is compared with various existing algorithms for proving the efficiency and minimum loss of the proposed algorithm.

USE OF MACHINE LEARNING TO FIND AND CLASSIFY BRAIN TUMORS

Dr. Raja Sarath Kumar Boddu

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 892-898

Brain tumour segmentation is one of the most critical and time-consuming jobs in the field of medical image processing since a human-assisted manual categorization may lead to incorrect prognosis and diagnosis. Furthermore, when there is a big quantity of data to be handled, it is a time-consuming job to say the least. There is a great deal of variation in brain tumours. There is a resemblance in appearance between tumour and normal tissues, which allows for the extraction of tumour areas from normal tissues. Images grow stubborn as time goes on. Using 2D Magnetic Resonance Imaging, we presented a technique for extracting brain tumours from brain scans. The Fuzzy C-Means clustering technique was used to cluster brain images (MRIs), which was then followed by conventional classifiers and other methods. A convolution neural network is a kind of neural network. The experimental research was conducted out on a real-time dataset including tumours of varying sizes, and Locations, forms, and varying picture intensities are all explored. In the conventional classifier section, we used six different traditional classifiers. Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Multilayer Perception (MLP), and Logistic Regression are examples of machine learning algorithms. Regression, Nave Bayes, and Random Forest are all machine learning techniques that have been incorporated in scikit-learn. Following that, we went on to Convolution Neural Network (CNN) is a kind of neural network that is built using Keras and Tensor flow since it produces superior results. Performance as compared to the conventional ones CNN had an accuracy rate of 97.87 percent in our research, which is very impressive. The In this article, the primary objective is to differentiate between normal and aberrant pixels using texture-based and statistical methods. Characteristics that are based on