Classification of brain disease using deep learning with multi-modality images

Abstract

Brain diseases is a wide range of disorders and diseases that affect the brain. They can change a person’s behavior, personality, and capacity for thought and function. CT images are more essential than conventional clinical tests for detecting brain hemorrhage accurately. MRI images of the brain can reveal even small abnormalities in the cranial region, helping providers diagnose a wide variety of conditions, ranging from brain stroke, cancers, aneurysms, and Alzheimer’s. This paper proposes a novel Fused dual neural (FDN) network for detecting brain cancer, stroke, aneurysms, and Alzheimer using Brain Medical Images (BMI) the combination of MRI and CT. In BMI, the adaptive bilateral filter reduces noise artifacts. Google Net is used to extract features from pre-processed MRI images, and Mobile Net is used to extract features from pre-processed CT images. The integration of extracted features from Google Net and Mobile Net is fused by the Wrapper method. Finally, the Deep Belief Network is employed for classifying brain stroke, cancer, Aneurysm, and Alzheimer’s diseases using BMI images. The quantitative analysis of the suggested method is determined using the parameters like specificity, recall, precision, F1 score, and accuracy. The proposed FDN achieves a high classification accuracy rate of 98.19%, 97.68%, 94.31%, and 93.82% for detecting stroke, cancer, Aneurysm, and Alzheimer respectively. The proposed FDN model improves the overall accuracy by 5.35%, 3.14%, 9.48%, 5.33%, and 0.55% better than Faster R-CNN, CNN, Inception-V3, DCNN, and Fine-tuning Network respectively.