Brain Tumor Detection through Thermal Imaging and MobileNET

TL;DR

This paper presents a MobileNET-based approach for brain tumor detection using thermal imaging, achieving high accuracy with reduced computational resources and faster processing compared to traditional methods.

Contribution

It introduces a novel, resource-efficient brain tumor detection model utilizing MobileNET and image processing, improving speed and accuracy over classical ML approaches.

Findings

Achieved 98.5% average accuracy in tumor detection.

Reduced computational resources and processing time.

Effective decision-making through image processing techniques.

Abstract

Brain plays a crucial role in regulating body functions and cognitive processes, with brain tumors posing significant risks to human health. Precise and prompt detection is a key factor in proper treatment and better patient outcomes. Traditional methods for detecting brain tumors, that include biopsies, MRI, and CT scans often face challenges due to their high costs and the need for specialized medical expertise. Recent developments in machine learning (ML) and deep learning (DL) has exhibited strong capabilities in automating the identification and categorization of brain tumors from medical images, especially MRI scans. However, these classical ML models have limitations, such as high computational demands, the need for large datasets, and long training times, which hinder their accessibility and efficiency. Our research uses MobileNET model for efficient detection of these tumors. The novelty of this project lies in building an accurate tumor detection model which use less computing re-sources and runs in less time followed by efficient decision making through the use of image processing technique for accurate results. The suggested method attained an average accuracy of 98.5%.