Although brain tumors are generally lethal, but the severity and the prognosis of the disease depends upon factors like Family history, Age and Race etc. Primary tumors are those that start in the brain region itself, whereas secondary tumors begin in another part of the body and spread to the brain region to develop into a brain tumor. Brain Tumors are basically of two types: Primary and secondary types 1. Primary symptoms of BT being headache and dizziness. Brain tumors may or may not be cancerous but nevertheless, they cause serious problems in the skull region because of the enormous pressure it puts on the cranium due to lack of space in the skull. With its current effectiveness, the model we propose represents a critical alternative for the statistical detection of brain tumors in patients who are suspected of having one.Ī group of aberrant brain cells, whether malignant or not, is referred to as a Brain Tumor (BT). The highest accuracy achieved was 95.01% by Net-2. Five different networks namely Net-0, Net-1, Net-2, Net-3 and Net-4 are proposed, and it is found that Net-2 outperformed the other networks namely Net-0, Net-1, Net-3 and Net-4. These modified graphs are given as the input matrices to a standard 26 layered CNN with Batch Normalization and Dropout layers intact. A standard pre-computed Adjacency matrix is used, and the input graphs were updated as the averaged sum of local neighbor nodes, which carry the regional information about the tumor. The objective of Graph Convolution is to modify the node features (data linked to each node) by combining information from nearby nodes. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26 layered CNN that takes in a Graph input pre-convolved using Graph Convolution operation. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster.
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