Deep Learning Unveils Hidden Insights: Advancing Brain Tumor Diagnosis

Priyanka Kaushik

doi:10.55938/ijgasr.v2i2.45

Authors

Priyanka Kaushik Chandigarh University https://orcid.org/0000-0003-4766-8772

DOI:

https://doi.org/10.55938/ijgasr.v2i2.45

Keywords:

DL, Brain Tumor, MRI, CNN-RNNs, GANs, Transfer Learning, Data Augmentation, Radiology

Abstract

Timely detection and treatment are crucial in managing brain tumors, a severe medical condition. MRI is a commonly used diagnostic tool to detect brain tumors. However, because of the complex structure of the brain and the wide range of tumors sizes and forms, MRI scan interpretation can be time-consuming and error-prone. The automated detection and segmentation of brain tumors has shown encouraging results with to recent developments in DL techniques. We suggest a CNN-RNNs and GANs based DL technique for brain tumor identification in this paper. Transfer learning and data augmentation techniques are used in the suggested method to train the CNN on a sizable dataset of MRI images labelled with tumor areas. The suggested strategy, according to experimental findings, is more accurate than the most advanced techniques now available for finding brain tumors. The suggested strategy has the potential to help radiologists identify brain tumors quickly and reliably, improving patient outcomes.

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