Engineering Data-Driven Approaches for Classifying Tumor Types

The right way to group growth types is very important for making cancer assessments and treatment plans more accurate. A usual way would be a histology study. The results can be different because they depend on physical research and personal opinion. Genetic and medical imaging data are used in this study to look into how data-driven methods, can be used to sort different types of cancers. We show an all-around method that builds a strong classification system by combining various datasets, such as MRI scans, CT scans, and genetic profiles. SVM, RF, and CNN were the machine learning techniques used to find traits and group tumours in the study. We use strict cross-validation to test these models' performance and see how well they can handle overfitting, how sensitive they are, how specific they are, and how well they can predict. The findings show that deep learning models, especially CNNs, were better at telling the difference between types of tumours than older machine learning methods. They were more stable and accurate in their classifications. We also talk about how important it is to use mixed models that combine DNA data with pictures to make classification more accurate. This method not only makes the detection process faster, but it also lets doctors make treatment plans that are specific to each type of cancer. The results show that methods based on data can change how cancers are found and lead to better, more personalised medical care systems.