Abstract: Brain tumours are mostly produced by aberrant brain cell development, which can harm the brain's structure and eventually progress to dangerous brain cancer. The proper detection of various disorders in the gorgeous MRI pictures is one of the primary obstacles in providing an early opinion to allow decisive therapy utilising a computer-backed opinion (CAD) system. In this study, a novel Deep Convolutional Neural Network (DCNN) framework for accurate diagnosis of glioma, meningioma, and pituitary tumours is suggested together with a three-step preprocessing method to improve the quality of MRI images. For quick training with a high literacy rate and simple initialization of the sub caste weights, the armature employs batch normalisation. The suggested armature is a lightweight computational model with a few convolutional, maximum-- pooling layers and training duplications.

Keywords: Brain tumors, deep convolutional neural network, image processing, MRI images.


PDF | DOI: 10.17148/IJARCCE.2023.12251

Open chat
Chat with IJARCCE