Proposal for Prediction of Brain Tumour and H...

Proposal for Prediction of Brain Tumour and Hemorrhage using aHybrid Approach

Author Name : Shruti Kolte, Pranay Sune, Satyajeet Pathare, Sudhanshu Gomase, Harshal Gothe

ABSTRACT Brain tumour and hemorrhages are critical brain diseases that require accurate and early diagnosis for effective treatment and management.This work proposes a hybrid deep learning and traditional machine learning approach for automated detection and diagnosis of brain tumours and hemorrhages using magnetic resonance imaging (MRI) scans. The developed system employs a combination of deep convolutional neural networks and engineered featurebased classifiers to leverage the representation learning capabilities of deep learning and the interpretability of traditional models. The deep learning models learn hierarchical feature representations directly from the MRI images while hand-crafted features provide expert domain knowledge. A fusion of predictions from both models is used to improve diagnostic accuracy. The system was trained and evaluated on a dataset of 3000 MRI scans categorized by tumour type and hemorrhage presence. Results demonstrate that the hybrid system outperforms either individual approach with 92% accuracy for tumour classification and 94% accuracy for hemorrhage detection. The integrated system provides accurate, automatic detection of critical brain disorders using MRI scans to assist healthcare professionals in early diagnosis and treatment planning. This work demonstrates the potential of hybrid AI systems for improving computer-aided diagnosis in healthcare