Multi-Class Brain Normality and Abnormality Diagnosis Using Modified Faster R-Cnn

Abstract

Background and Objective: The detection and analysis of brain disorders through medical imaging techniques are extremely important to get treatment on time and sustain a healthy lifestyle. Disorders cause permanent brain damage and alleviate the lifespan. Moreover, the classification of large volumes of medical image data manually by medicine experts is tiring, time-consuming, and prone to errors. This study aims to diagnose brain normality and abnormalities using a novel ResNet50 modified Faster Regions with Convolutional Neural Network(R-CNN) model. The classification task is performed into multiple classes which are hemorrhage, hydrocephalus, and normal. The proposed model both determines the borders of the normal/abnormal parts and classifies them with the highest accuracy. Methods: To provide a comprehensive performance analysis in the classification problem, Machine Learning(ML) and Deep Learning(DL) techniques were discussed. Artificial Neural Network(ANN), AdaBoost(AB), Decision Tree(DT), Logistic Regression(LR), Naive Bayes(NB), Random Forest(RF), and Support Vector Machine(SVM) were used as ML models. Besides, various Convolutional Neural Network(CNN) models and proposed ResNet50 modified Faster R-CNN model were used as DL models. Methods were validated using a novel brain dataset that contains both normal and abnormal images. Results: Based on results, LR obtained the highest result among ML methods and DenseNet201 obtained the highest results among CNN models with the accuracy of 84.80% and 85.68% for the classification task, respectively. Besides, the accuracy obtained by the proposed model is 99.75%. Conclusions: Experimental results demonstrate that the proposed model has yielded better performance for detection and classification tasks. This artificial intelligence(AI) framework can be utilized as a computer-aided medical decision support system for medical experts.