Predictive and perspective analysis of cancer image data set using machine learning algorithms

Classification and prediction of the images are fairly easy task for humans, but it takes more effort for a machine to do the same. Machine learning helps to attain this goal. It automates the task of classifying a large collection of images into different classes by labelling the incoming data and recognizes patterns in it, which is subsequently translated into valuable insights. The aim of this paper is to classify the image data set of five cancer types, namely Osteosarcoma, Prostate Cancer, Brain Cancer, Breast Cancer and Acute Myeloid Leukaemia. Furthermore, the prediction of Osteosarcoma case for one of the four classes of tumor namely Non tumor, Non-Viable tumor, viable tumor, Viable: Non-Viable tumor has to be done. The quantitative analysis is done using various machine learning libraries of python. The three classification algorithms used for image analysis are random forest, SVM, and logistic regression. The metrics used for performing perspective analysis are precision, recall and F1 Score. The results show that the random forest algorithm has performed best amongst the three classification algorithms when given with less complicated scenario, with prediction accuracy, precision, recall and f1 score of 100%. But the performance of every classification algorithm degrades when provided with the cases of Osteosarcoma which has got more complicated scatter graph. However, the logistic regression retains its performance by predicting tumor cases with 99% accuracy.