The detection and treatment of brain tumor remain a significant challenge for neurosurgeons. Early detection of tumor cells is essential for improving patient outcomes, as late diagnosis can result in higher mortality rates. Automated cancer cell detection methods can assist neurosurgeons in identifying tumour and reduce their workload. The suggested method in your research, which utilizes a convolutional neural network (CNN) with Median filtering and texture feature extraction, may offer a promising approach to automated tumour cell detection. Median filtering is a noise reduction technique that can help remove unwanted noise from medical images, while texture feature extraction can help to identify subtle patterns in the images that may indicate the presence of tumour cells. The CNN-based system can learn from a large dataset of medical images to detect cancer cells accurately. Various techniques are used for tumour segmentation, such as thresholding, region-growing, clustering, and deformable models. Finally, in the classification phase, machine learning techniques are used to classify the segmented tumour region as either benign or malignant.

Convolutional Neural Networks, MRI Image