Automated Brain Tumor Detection Using DenseNet121: A Deep Learning Approach for Enhanced Diagnosis in Medical Imaging

Dr. Deepika Saravagi

Dr. Deepika Saravagi Assistant Professor, Patkar Varde College, Goregaon, mumbai, Maharaashtra

Keywords: Brain Tumor Detection, DenseNet121, MRI

Abstract

Brain tumors are among the most serious and potentially fatal conditions affecting neurological health, necessitating quick and accurate diagnostic methods. Traditional diagnostic techniques rely on expertly analyzing MRI data, which can be time-consuming and subject to variation. Utilizing its feature propagation architecture to improve classification accuracy in complex medical imaging, the DenseNet121 model for automated brain tumor identification is examined in this research. Using a diverse MRI dataset, the model was trained and validated, achieving 99% accuracy. According to our research, DenseNet121 is a very effective tool for detecting brain tumors, showing great potential for practical use in supporting radiologists and accelerating diagnosis.

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