Brain Tumor Classification using Machine Learning

This paper presents a technical analysis of tumor data with Machine Learning and Classification Approach. Feature parameters which are dependent for classification of tumor are used for analyzing and classifying the class of tumor. In the classification of tumor, KNN-Classifier is implemented with cross validating accuracy score and tuning hyper parameters. Experimental simulation for best average score for K makes it to the cross validation. Approaching the prediction with the best accuracy score, hyper parameters of KNN Classifier states the best score. Using Principal Component Analysis on the data, miss-classification of tumor class in data is visualized.

Aims: To declare and analyse tumor data from the source of MRI, CT scan, etc. for medication of tumor. To utilize smart predictions for the upcoming tumor patients using Machine Learning.

Study Design: Tumor classification using K Nearest Neighbor algorithm and analysis of the miss-classification.

Methodology: We included 11 different studies and research papers which were relevant with tumor classification. Research papers include classification of tumors with different supervised learning approaches. Our proposed analysis and classification give visualization of two classes of tumor.

Results: The Project results in classification of tumor data using Machine Learning and analyzing the miss-classification of tumor. In implementation of KNN Algorithm, the accuracy score after cross validation and tuning K values is 0.97. The confusion matrix shows 4 false positives and 1 false negative value in testing.

Conclusion: Less miss-classification of tumor results best accuracy score and more efficient working on testing data. Visualizing the classification with 3-dimensional scatter plots made the analysis accurate.