Description

Detection and Classification of cancer cells in MRI Images-Image processing project-Deep Learning -Matlab

An accurate classification of human cancer, including its primary site, is important for better understanding of cancer and effective therapeutic strategies development. The available big data of somatic mutations provides a great opportunity to investigate cancer classification using machine learning. In this research primary sites classification using machine learning and somatic mutation data is proposed. Here, in this research the patterns exploration of 1,760,846 somatic mutations identified from 230,255 cancer patients along with gene function information using support vector machine is proposed. In this a multiclass classification experiment over the 17 tumor sites using the gene symbol, somatic mutation, chromosome, and gene functional pathway as predictors for 6,751 subjects is to be performed. Adding the information of mutation and chromosome will improve the result. Among the predictable primary tumor sites, the prediction of five primary sites (large intestine, liver, skin, pancreas, and lung) could achieve the performance with more in F-measure. expected outcome of this research is that more accurate result will be generated for Detection and Classification of cancer cells in MRI Images.