Exploring Deep Learning for Efficient Data Management in Brain Tumor Classification: An Overview

The effectiveness of brain tumor diagnosis with artificial intelligence (AI) based systems depends on effective data management (DM). The research on medical image analysis is increasingly focused on employing deep learning (DL) to detect and classify brain tumors. Presently, medical images are being evaluated and tumors are being classified using DL. Within DL a popular method, Convolutional Neural Networks (CNNs), is employed with great accuracy to detect brain tumors in medical images. The creation of these models involved utilizing extensive datasets of brain MRI scans, resulting in their exceptional accuracy in detecting the presence of tumors. The models have undergone testing and training utilizing several publicly accessible data, for example Kaggle, the Brain Tumor Segmentation (BraTS) dataset, etc dataset. Scientists have devised multiple techniques for data management (DM) involving machine learning (ML) to preprocess the data, augment it, and optimize the models in order to enhance their accuracy even further. In general, the topic of utilizing DM with the help of ML and DL in identifying brain tumors is quickly expanding. It holds great potential for improving patient outcomes. DL techniques have significantly improved the classification and identification of brain cancers using diverse datasets.