Segmentation of MRI images for brain cancer detection

Chehade, Wassim El Hajj; Kader, Riham Abdel; El-Zaart, Ali

doi:10.1109/icoiact.2018.8350721

Cited by 10 publications

(9 citation statements)

References 11 publications

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“…Diversity in data samples is consider as a key parameter for better training and overall better prediction results of algorithm.If there is lack of diversity in data then it is understood that there will be bias in training process and possibly overfitting. In articles [45,47,49,52,56] researchers mentioned the challenge of lack of diversity of data in their datasets. Misclassification rates were higher and overall poor prediction of images was reported in all such articles.…”

Section: Diversity Of Data Samplesmentioning

confidence: 99%

“…In [42] researcher reported that after augmentation the data histograms were asymmetric and hence during segmentation the optimal threshold value was very difficult to find. In [45,47] researcher faced the challenge of computational complexity due to augmentation because it induced fine grained noisy data into the dataset. In [49] the misclassification rates were very high after augmentation of data due to bias and increased feature dimensionality.…”

Section: Augmentation Of Datamentioning

confidence: 99%

“…The challenge of optimization deals with the best use of resources available at hand. Optimization problem in above review section was faced in [42,43,47,51,[53][54][55][56] by researchers. The problem of feature optimization was encountered in [43,51,56] by researchers.…”

Section: Optimizationmentioning

confidence: 99%

“…The problem of feature optimization was encountered in [43,51,56] by researchers. Feature optimization guarantees that the classification of data is best done with these features among all other features extracted from data.In [47,51,54,55] various parameters of classifiers were identified and optimized so that performance of these classifiers may be enhanced. In [42], the challenge of finding optimal thresh hold value was identified by the researchers because of the non-symmetric histogram image data.…”

Section: Optimizationmentioning

confidence: 99%

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A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

Manhas

Gupta

Roy

2021

Arch Computat Methods Eng

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Cancer is one of the most deadly diseases diagnosed among the population across the globe so far. The number of cases is increasing at a high pace each year that subsequently leads to the advancement in different diagnosis tools and technologies to handle this pandemic. Significant increase in the mortality rate worldwide leads tremendous scope to device and implement latest computer aided diagnostic systems for its early detection. The one among such techniques is machine learning coupled with medical imaging modalities. This combination has proven to be efficient in diagnosing various medical conditions in cancer diagnosis. Current study presents a review of different machine learning techniques applied on imaging modalities for cancer diagnosis from 2008 to 2019. This study focuses on diagnosis of five most prevalent and deadly cancers i.e., cervical cancer, oral cancer, breast cancer, brain cancer and skin cancer. Extensive and exhaustive review was carried out after going through different research papers, research articles and book chapters published by reputed international and national publishers such as Springer Link, Science Direct, IEEE Xplore Digital library and PubMed. A number of conference proceedings have also been included subject to the fulfilling of our quality evaluation criteria. This review article provides a comprehensive overview of machine learning approaches using image modalities for cancer detection and diagnosis with main focus on challenges being faced during their research. Majority of the challenges are identified based on the use of potential machine learning based approaches, image modalities, features and evaluation metrics. This review not only identified challenges but also ear mark and present the new research opportunities for researchers working in this field. It has been widely observed that traditional machine learning algorithms Like SVM, GMM performed excellent in classification whereas the deep learning has dominated the field of medical image analysis to a greater extent. It is evident from the literature survey that the researchers have achieved the accuracies of 100% in classification of cancerous and normal tissue images using different machine learning techniques. This article will provide an insight to the researchers working in this domain to identify which machine learning technique work best on what type of data set, selection of features, various challenges and their proposed solutions in solving this complex problem. Limitations and future research opportunities in the field of implementing different machine learning techniques in cancer diagnosis and classification is also presented at the end of this review article.

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Section: Diversity Of Data Samplesmentioning

confidence: 99%

Section: Augmentation Of Datamentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

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A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

Manhas

Gupta

Roy

2021

Arch Computat Methods Eng

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“…In the past ten years there is a tremendous development in the field of brain MRI segmentation and edge detection to detect tumors [5], [6], [7], [8].Algorithms which are implemented in software packages are very expensive and only affordable to extravagant sanatoriums and are not does not have friendly and easy to use human interface. In this work we have developed and presented a free to use graphic mode computer executable software tools that run without human interference.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Screening and Early Detection of Level Of Tumor And Survival Time Of Patient Using MRI Images Modality And Computational Tool

D.C¹,

Sadiya²

2019

IJRAT

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Brain tumors are unwonted and unbounded multiplication of cells. Brain tumor reports for 85% to 90% of all primary Central Nervous System tumors. There is a need for bio-medical imaging computational tools that performs processing and extracting clinically relevant facts and figures from patient's brain MRI images. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, cellular structure and vascular supply, making it an important tool for the effective diagnosis, treatment and monitoring of the disease. This paper presents a computer aided detection tool for brain tumor using brain MRI images and assists the neurologists to plan the further treatment. This tool automatically perform pre-processing of MRI images using Gabor filter, edge detection and feature extraction using Canny edge detector algorithm and finally classify the tumor using KNN classifier. In addition, this computational tool aids in classifying the stage of tumor and approximate survival time of patients. The experimental results of proposed technique shows better results with 93% accuracy compared to state of art methods.

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The Role of Bioinformatics and Imaging Models in Tumorigenesis and Treatment Response of Brain and Spinal Cord Neoplasm

Verma

Shanker

Patel

et al. 2023

Advances in Experimental Medicine and Biology

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Segmentation of MRI images for brain cancer detection

Cited by 10 publications

References 11 publications

A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

Brain Tumor Screening and Early Detection of Level Of Tumor And Survival Time Of Patient Using MRI Images Modality And Computational Tool

The Role of Bioinformatics and Imaging Models in Tumorigenesis and Treatment Response of Brain and Spinal Cord Neoplasm

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