<p>The improvement of Artificial Intelligence
(AI) and Machine Learning (ML) can help radiologists in tumor diagnostics
without invasive measures. Magnetic resonance imaging (MRI) is a very useful
method for diagnosis of tumors in human brain. In this paper, brain MRI images
have been analyzed to detect the regions containing tumors and classify these
regions into three different tumor categories: meningioma, glioma, and
pituitary. This paper presents the implementation and comparison of various
enhanced ML algorithms for the detection and classification of brain tumors. A
brain tumor is the growth of abnormal cells in the human brain. Brain tumors
can be cancerous or non-cancerous. Cancerous or malignant brain tumors can be
life threatening. Hence, detection and classification of brain tumors at an
early stage is extremely important. In this paper, enhanced ML algorithms have
been implemented to predict the presence or the absence of brain tumors using
binary classification and to predict whether a patient has brain tumor or not
and if he does, detect the type of brain tumor using multi-class
classification. The dataset that has been used to perform the binary
classification task comprises of two types of brain MRI images with tumor and
without tumor. Here nine ML algorithms namely, Support Vector Machine (SVM),
Logistic Regression, K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree
(DT) classifier, Random Forest classifier, XGBoost classifier, Stochastic
Gradient Descent (SGD) classifier and Gradient Boosting classifier have been
used to classify the MRI images. A comparative analysis of the ML algorithms
has been performed based on a few performance metrics such as accuracy, recall,
and precision, F1-score, AUC-ROC curve and AUC-PR curve. Gradient Boosting
classifier has outperformed all the other algorithms with an accuracy of 92.4%,
recall of 94.4%, precision of 85%, F1-score of 89.5%, AUC-ROC of 97.2% and an
AUC-PR of 91.4%. To address the multi-class classification problem, four ML
algorithms namely, SVM, KNN, Random Forest classifier and XGBoost classifier
have been employed. In this case, the dataset that has been used consists of
four types of brain MRI images with glioma tumor, meningioma tumor, and pituitary tumor and
with no tumor. The performances of
the ML algorithms have been compared based on accuracy, recall, precision and
the F1-score. XGBoost
classifier has surpassed all the other algorithms in terms of accuracy,
precision, recall and F1-score. XGBoost has produced an accuracy of 90%,
precision of 90%, and recall of 90% and F1-score of 90%.</p>
Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment
