ISSN 2063-5346
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1 st Amit Thakur, 2 nd Pawan Kumar Patnaik
» doi: 10.48047/ecb/2023.12.9.07


Technology advancements have the power to impact every area of human existence. Technology has, for instance, significantly benefited human civilization when used in medicine. In this piece, we concentrate on using technology to help treat brain tumors, one of the most prevalent and deadliest illnesses ever. One of the malignant tumors that is identified most often in people of all ages is a brain tumor. Using brain MRI data, the most sophisticated deep learning technique, a CNN (Convolutional Neural Network), was utilized to find a tumor. There are still problems with the laborious training process, however. One of the most difficult components of classifying brain tumors is identifying the kind of tumor and avoiding it. In this paper, we did a thorough analysis of the existing attempts to apply various deep learning techniques to MRI data and identified the domain’s current obstacles before identifying possible future approaches. Deep learning networks’ exponential expansion has made it possible for humans to handle challenging jobs, even in the intricate area of medicine. This article provides an overview of studies on the segmentation of brain tumors using MRI images published between 2018 and 2023. However, in order for the networks to be highly generalizable and with good performance, applying these models needs a big corpus of data. For researchers in the biological and machine learning sectors, several routes for future study are finally presented

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