Detection of Brain Tumor based on Features Fusion and Machine Learning

2018 ◽  
Author(s):  
Javeria Amin ◽  
Muhammad Sharif ◽  
Mudassar Raza ◽  
Mussarat Yasmin
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Features Fusion

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

scholarly journals A tongue features fusion approach to predicting prediabetes and diabetes with machine learning

2021 ◽  
Vol 115 ◽  
pp. 103693
Author(s):  
Jun Li ◽  
Pei Yuan ◽  
Xiaojuan Hu ◽  
Jingbin Huang ◽  
Longtao Cui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Features Fusion ◽  
Fusion Approach

scholarly journals On the Methods for Detecting Brain Tumor from MRI images

2020 ◽  
Vol 9 (9s) ◽  
pp. 37-44
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Imaging ◽  
Tumor Detection ◽  
Gray Level ◽  
Wide Spread ◽  
Emerging Trends ◽  
Day By Day ◽  
High Diversity

Brain tumor detection from MRI images is a challenging process due to high diversity in the tumor pixels of different peoples. Automatic detection has got wide spread acclaim because the manual detection by experts is time consuming and prone to error in judgment. Due to its high mortality rate, detection of tumor automatically is a new emerging technique in bio medical imaging. Here we present a review of few methods from simple thresholding to advanced deep learning methods for segmentation of tumor from MRI data. The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning. The results of various methods are compared to find the best methods available. As medical imaging methods have improving day by day this review will help to understand emerging trends in brain tumor detection.

Automated Brain Tumor Detection Model Using Modified Intrinsic Extrema Pattern based Machine Learning Classifier

2021 ◽  
Author(s):  
K. Sakthidasan Sankaran ◽  
A S Poyyamozhi ◽  
Shaik Siddiq Ali ◽  
Y. Jennifer
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Detection Model ◽  
Learning Classifier

Advancements of MRI-Based Brain Tumor Segmentation from Traditional to Recent Trends- A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Padmapriya Thiyagarajan ◽  
Sriramakrishnan Padmanaban ◽  
Kalaiselvi Thiruvenkadam ◽  
Somasundaram Karuppanagounder
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Human Head ◽  
Medical Community ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Learning Methods ◽  
Brain Tumor Segmentation ◽  
Mri Scans

Background: Among the brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is a very challenging task due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques, to deep learning through machine learning on MRI of human head scans. Method: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed. Results: The prime motivation of the paper is to instigate the young researchers towards the development of efficient brain tumor segmentation techniques using conventional and recent technologies. Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mostly applied for brain tumor detection, whereas deep learning methods were good at tumor substructures segmentation.

scholarly journals Intraoperative Detection of Pediatric Brain Tumor Margins Using Raman Spectroscopy.

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Rashad Jabarkheel ◽  
Jonathon J Parker ◽  
Chi-Sing Ho ◽  
Travis Shaffer ◽  
Sanjiv Gambhir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Tissue ◽  
Pediatric Brain Tumor ◽  
Tissue Samples ◽  
Tumor Margins ◽  
Pediatric Brain ◽  
Tumor Debulking

Abstract INTRODUCTION Surgical resection is a mainstay of treatment in patients with brain tumors both for tissue diagnosis and for tumor debulking. While maximal resection of tumors is desired, neurosurgeons can be limited by the challenge of differentiating normal brain from tumor using only microscopic visualization and tactile feedback. Additionally, intraoperative decision-making regarding how aggressively to pursue a gross total resection frequently relies on pathologic preliminary diagnosis using frozen sections which are both time consuming and fallible. Here, we investigate the potential for Raman spectroscopy (RS) to rapidly detect pediatric brain tumor margins and classify brain tissue samples equivalent to histopathology. METHODS Using a first-of-its-kind rapid acquisition RS device we intraoperatively imaged fresh ex vivo pediatric brain tissue samples (2-3 mm × 2-3 mm × 2-3 mm) at the Lucille Packard Children's Hospital. All imaged samples received standard final histopathological analysis, as RS is a nondestructive imaging technique. We curated a labeled dataset of 575 + unique Raman spectra gathered from 160 + brain samples resulting from 23 pediatric patients who underwent brain tissue resection as part of tumor debulking or epilepsy surgery (normal controls). RESULTS To our knowledge we have created the largest labeled Raman spectra dataset of pediatric brain tumors. We are developing an end-to-end machine learning model that can predict final histopathology diagnosis within minutes from Raman spectral data. Our preliminary principle component analyses suggest that RS can be used to classify various brain tumors similar to “frozen” histopathology and can differentiate normal from malignant brain tissue in the context of low-grade glioma resections. CONCLUSION Our work suggests that machine learning approaches can be used to harness the material identification properties of RS for classifying brain tumors and detecting their margins.

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