Pituitary Tumor Classification using Machine Learning

One of the more extreme problems that influences the two, kids and grown-ups is a cerebrum cancer. 85 to 90 percent of all essential Focal Sensory system malignancies are mind cancers. Around 11,700 patients are given a mind cancer finding every year. For the individuals who have a harmful cerebrum or Central Nervous System growth, the 5-year endurance rate is generally 34% for guys and 36% for ladies or women. In this paper, it is used with the classifiers to detect the tumor in human they are CNN, KNN, Logistic regression and SVM algorithms. Benign, malignant, pituitary, and other types of brain tumors are all classified. KNN-Classifier is used to categories tumors, and cross-validating accuracy score and hyperparameter tuning are used. The cross validation includes the experimental simulation with the best average score for K. Hyper parameters of the KNN Classifier state the best score when approaching the prediction with the highest accuracy score. Convolutional neural networks (CNNs) are used in our method to investigate discriminative information while integrating multi-layer dictionary learning. From the comparisons it is been analyzed that CNN Classifier has the best performance and achieved with 85.24 % accuracy.