Authors : Bhavika Tiwari; Surbhi Sarode; Yashashree Shinde; Sonal Kadam

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/yc7y77wz

Scribd : https://tinyurl.com/3te5ed3n

DOI : https://doi.org/10.38124/ijisrt/25apr2315

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The early detection of brain tumors is crucial for effective treatment and improved patient outcomes, but traditional methods often fall short in terms of accuracy and efficiency. This project addresses these limitations by developing an advanced brain tumor detection system using a combination of machine learning and deep learning techniques. The proposed system integrates several key functionalities: imaging technique identification, modality-specific tumor detection, and automated report generation. The system begins with classifying the imaging technique used (e.g., MRI, CT) to apply the most suitable detection model for each modality. It then employs deep learning algorithms to detect and classify tumors, while also addressing common issues such as class imbalance through advanced data augmentation and resampling techniques. An additional feature is the integration of automated report generation, which creates preliminary diagnostic reports based on detected tumors, providing valuable context for clinicians. By combining these approaches, the system aims to enhance diagnostic accuracy, improve clinical workflows, and ensure a comprehensive analysis of brain tumor data. This project demonstrates the potential of integrating multiple machine learning techniques to create a robust tool for early and precise brain tumor detection, contributing to more effective and timely treatment options in medical practice.

Keywords : Brain Tumor Detection; Deep Learning; Machine Learning; Medical Imaging; Modality Classification; Automated Report Generations.

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