Automated Brain Tumor Detection Using Hybrid CNN - Models from T1 – weighted MRI Scans

Detecting and classifying brain tumours is essential for early diagnosis and effective treatment planning, significantly enhancing patient outcomes. This research presents a deep learning-based approach that utilizes T1-weighted MRI data to automatically identify and classify brain tumours, distinguishing between normal and abnormal cases. The proposed methodology consists of four key steps: pre-processing, segmentation, feature extraction, and classification. In the pre-processing stage, image quality is enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE) to boost contrast, along with a Gaussian filter to reduce noise. Tumour segmentation is achieved through thresholding, which effectively isolates the tumour regions. For feature extraction, a Convolutional Neural Network (CNN) captures high-dimensional features that are vital for classification. To accurately differentiate between normal and abnormal tumours, an Artificial Neural Network (ANN) is employed for classification. The effectiveness of the proposed technique is evaluated based on performance metrics such as time, accuracy, and peak signal-to-noise ratio (PSNR). The obtained parameters are compared with existing techniques to highlight improvements in detection and classification performance. Among the tested images, the best result achieved a PSNR of 13.015 dB, an accuracy of 99.231%, and a computational time of 1.267 ms, demonstrating the efficiency and reliability of the proposed method for brain tumor detection and classification. Overall, this approach provides an effective and automated method for detecting brain tumours, aiding in clinical decision-making and diagnosis.