Enhanced Brain Tumor Detection and Stage Prediction through a Unified CNN-Based Framework

Abstract

Abstract: A brain tumor refers to an abnormal proliferation of cells in the brain, which can be categorized as either benign (noncancerous) or malignant (cancerous). Benign tumors exhibit slow growth and remain localized without spreading to other parts of the body. In contrast, malignant tumors grow rapidly and can metastasize to other regions of the brain or spinal cord. The occurrence of brain tumors is not restricted to specific brain regions, and their impact on bodily functions varies depending on their location. Initially, we gather a dataset consisting of medical images depicting brain scans with and without tumors. These images undergo pre-processing procedures to ensure their suitability for training the CNN model. Our system employs fully connected layers and a SoftMax layer to classify tumors as either tumorous or non-tumorous. To train the CNN model, we employ backpropagation and gradient descent algorithms on the pre-processed dataset. The objective is to optimize the model parameters, reducing the classification error on the training set. Furthermore, by incorporating fine-tuning techniques such as hyperparameter optimization and regularization, our CNN model also demonstrates the ability to predict the stage of a brain tumor.