Integrated System for Brain Tumour Detection and Classification

In this research, an integrated system for brain tumor detection and classification using MRI images has been proposed. The system combines image preprocessing, feature extraction, segmentation, and classification techniques to accurately identify tumor regions in brain MRI scans. Image preprocessing techniques such as noise removal and image enhancement improve the quality of the MRI images and help in better analysis. The proposed model uses Convolutional Neural Network (CNN) to detect and classify brain tumors. The experimental results demonstrate that the system achieves high accuracy in identifying tumor and non-tumor images. Performance evaluation metrics such as accuracy, sensitivity, and specificity show that the proposed approach is effective for medical image analysis. Therefore, the developed system can assist medical professionals in early diagnosis of brain tumors and improve decision making in clinical environments. In the future, the system can be enhanced by using larger datasets and advanced deep learning techniques to further improve detection accuracy and reliability.

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