A Machine Learning Approach in Medical Image Analysis for Brain Tumor Detection

2020 ◽  
pp. 127-135
Author(s):  
K. Aswani ◽  
D. Menaka ◽  
M. K. Manoj
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Brain Tumor ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Tumor Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine Learning Approach-Based Gamma Distribution for Brain Tumor Detection and Data Sample Imbalance Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12-19 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
P. Mohamed Shakeel ◽  
Azza S. Hassanein ◽  
Priyan Malarvizhi Kumar ◽  
Gokulnath Chandra Babu
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Gamma Distribution ◽  
Tumor Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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