Image classification of intracranial tumor using deep residual learning technique.

Classifying brain tumours is essential for diagnosing tumour progression and planning effective treatments. Different imaging modalities are used to diagnose brain tumours. The opposite is true for magnetic resonance imaging (MRI), which has gained widespread use due to its superior image quality and the fact that it does not require ionizing radiation. Image classification of intracranial tumors using deep residual learning technique is an application of deep learning in the field of medical imaging analysis. It involves using convolutional neural networks (CNNs) to automatically classify brain images into different categories based on the presence or absence of tumors. ResNet is a deep neural network that addresses the problem of vanishing gradients during training of very deep networks.The deep learning subfield of machine learning has recently shown remarkable success, especially in classification and segmentation. We trained a deep residual network using picture datasets to distinguish between several brain cancers. The information generated by MRI scans is extensive. A radiologist analyses these images. The three most common brain tumours are meningioma, glioma, and pituitary tumour. Brain tumours are complex diseases, and a manual examination may be fraught with error. Experimental outcomes based on various techniques under augmentation with image-based datasets are presented. The accuracy with no augmentation is about 98% and under augmentation is approximately 99.08%.By leveraging deep residual learning techniques, image classification of intracranial tumors can benefit from the ability of deep neural networks to automatically learn complex representations from raw image data.Classification methods that use machine learning to automate the process have proven superior to human curation. Thus, we present a system that can identify and classify utilizing deep CNN-based residual networks. [ABSTRACT FROM AUTHOR]