Published Paper Details:

Machine learning in medical imaging plays the greatest disruptive technology in decades. It is being emerged not only to identify cancerous tumors at an earlier stage, but also detect and classify lesions, analyze data, reconstruct images, and more. Cancer is one of the heterogeneous disease consists of many different subclasses. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research for facilitating the subsequent clinical management of patients. Researchers, healthcare organizations, companies from biomedical and bioinformatics look forward for improving clinical outcomes for cancer patients and those who may not by using diagnostic and prognostic biomarkers. But the challenge is distinguishing the mutations that contribute to tumor growth (drivers) from the neutral mutations (passengers). Currently this interpretation of genetic mutations is being done manually based on evidence from text-based clinical literature. This article is focusing on analyzing the performance of multiclass classification algorithms for genetic features.