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Brain Tumor Detection Using Machine Learning and Deep Learning: A Review.

Venkatesh S Lotlikar¹, Nitin Satpute², Aditya Gupta¹

¹Department of E & TC Engineering, College of Engineering, Pune, India.

Current Medical Imaging

|September 25, 2021

Summary

This summary is machine-generated.

Early brain tumor detection using advanced imaging and machine learning is crucial. This review analyzes techniques over 15 years, comparing methods and discussing future clinical challenges for better outcomes.

Keywords:

Brain tumor convolutional neural networks deep learning machine learning magnetic resonance imaging preprocessing

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Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Computational Pathology

Background:

Brain tumors have a high mortality rate (76% by IARC).
Early detection and treatment are vital for patient survival.
Advancements in medical imaging (MRI, CT) enable automated tumor detection via computer-aided design.

Purpose of the Study:

To conduct an exhaustive review of brain tumor detection techniques over the past 15 years.
To present a detailed comparative analysis of preprocessing, machine learning, and deep learning methods.
To discuss clinical challenges and future research directions in automated tumor detection.

Main Methods:

Review of machine learning and deep learning techniques, particularly Convolutional Neural Networks (CNNs).
Analysis of image preprocessing methods for medical scans (MRI, CT).
Comparative study of adopted techniques over the last 15 years.

Main Results:

Machine learning and deep learning, especially CNNs, show significant promise for analyzing complex medical image data.
A comparative analysis of various techniques highlights their strengths and weaknesses.
Identification of challenges and future research scopes in the field.

Conclusions:

Automated tumor detection using AI offers a promising avenue for improving early diagnosis and patient outcomes.
Further research is needed to address clinical challenges and refine AI-driven detection methods.
This review provides a comprehensive overview and roadmap for future work in brain tumor detection.