Region of interest

Normalization (sociology)

Support vector machine

Pituitary adenoma

Classifier (UML)

Computer science

Anthropology

Radiology

Pattern recognition (psychology)

Brain tumor

Sociology

Artificial intelligence

Adenoma

Medicine

Artificial neural network

Feature extraction

Magnetic resonance imaging

Pathology

Learning vector quantization

Informatics in Medicine Unlocked

In this era of rapid technological advancement, computer vision in medical analysis and diagnosis has a profound significance in detecting and classifying anomalies or diseases in the human body. This study proposes an ensemble learning method to classify brain tumors or neoplasms (i.e. glioma, meningioma, pituitary adenoma) and auto-immune disease lesion (i.e. multiple sclerosis) using magnetic resonance imaging (MRI) of brain tumors and multiple sclerosis patients. The method includes pre-processing, feature extraction, feature selection, and classification. The pre-processing phase uses region of interest (ROI) of both tumor and lesion, Collewet normalization, and Lloyd-max quantization. The base learner is designed with a support vector machine (SVM) classifier and prediction model with majority voting. Our proposed system operates well in contrast to other state-of-the-art methods, with the weighted sensitivity, specificity, precision, and accuracy of 97.5%, 98.838%, 98.011%, and 98.719%, respectively. Experimental results also show that the overall training and testing accuracy of our proposed model are 97.957% and 97.744% respectively. The findings may infer an unprecedented step for detecting the presence of lesions coexisting with the tumors in neuro-medicine diagnosis.

Classification of brain tumors and auto-immune disease using ensemble learning