Certain investigations on image and gene expression data for multiclass cancer classification using machine learning techniques

Abstract

Cancer diagnosis at its earlier stage is crucial to provide clinical solutions. While newlineanalyzing the clinical data for diagnosing any types of cancer or abnormal nodules that newlineare the major cause of cancer, one of the most dominating problems faced by the newlineresearchers is data redundancy and inconsistency. Even though there are several feature newlineextraction and classification techniques provided by Machine Learning (ML) that are newlineused for extracting the maximum information from the data, sometimes such a newlinetechnique also fails in predicting and classifying the absolute information from the newlineredundant data. newlineIn the case of cancer identification and classification, the term redundancy or newlineinconsistency can be quoted as a state where the normal cell turns into a cancerous cell newlineor a cancerous cell that is predicted as a normal cell. The data that can be considered for newlineanalysis and classification, in general, can be as image data or abstract feature sets newlineobtained after certain image processing techniques. Dealing with oncology using ML newlinetechniques will be more effective, but the underlying challenge in such hybridization is newlineintegrating and creating a clinical model. In earlier times only statistical methodologies newlineare adopted to find solutions for clinical data. newlineIn recent times, the enormous growth and advancement reached by Artificial newlineIntelligence (AI) especially due to the numerous types of research that are performed newlineusing various ML techniques has attracted the mass attention of clinical and data newlinescientists. While considering prediction and identification as a generalized newlinephenomenon, ML performs better traversal by calculating the number of insights from newlinethe given data. The prediction process involves identifying the parameter weights that newlinecan be interrelated with the extracted outcome. Identification according to the ML newlineperspective is a generalized concept that can be utilized in several real-time application newlineareas including clinical, sensors, scientific, stock market, and many other ML newlineapplication a