Critical Analysis of Brain Magnetic Resonance Images Tumor Detection and Classification Techniques

Ullah, Zahid; Lee, Suhyun; An, Donghyeok

doi:10.14569/ijacsa.2020.0110156

Cited by 4 publications

(3 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it has become crucial to use CAD technology. The two primary imaging modalities used to detect brain tumors are MRI and CT [26]. A study suggested a way to detect and diagnose lung nodules as soon as feasible in order to treat them successfully and lessen the burden on patients and the healthcare system.…”

Section: -2022mentioning

confidence: 99%

Computer-Aided Design on Image Detection: A Chronology Review

Maziah Mahmud,

Wan Azani Mustafa,

Abd Fatah Wahab

2023

ARASET

View full text Add to dashboard Cite

In order to conduct research and diagnose diseases, image processing is essential. Medical professionals frequently segment images for pre- and post-surgery decisions, which are necessary for treatment planning. In the medical research field, segmentation is the core subject of several studies. Computer-aided detection (CAD) is utilised to achieve the highest level of classification accuracy and may be used to identify tissues growing abnormally. For the purpose of finding abnormalities, magnetic resonance imaging (MRI) is an effective approach, but it takes time and requires a fair amount of human resources. This approach, however, was problematic for slicing data related to the interior surfaces of cavity structures, for instance, the human skull. As a result, a ray casting algorithm was used to create a software programme. The most significant problem with segmentation techniques for x-ray images is seed point selection. An object's surface structure is described by a three-dimensional (3D) surface structure graph (SSG) that was created during segmentation. Ultrasound image detection is critical today. The model can be further modified using this CAD software so that it can be reproduced on a rapid prototyping device in the STL file format. The suggested deep learning method is exceptionally effective in accurately detecting faults in each layer, according to experimental data.

show abstract

Section: -2022mentioning

confidence: 99%

Computer-Aided Design on Image Detection: A Chronology Review

Maziah Mahmud,

Wan Azani Mustafa,

Abd Fatah Wahab

2023

ARASET

View full text Add to dashboard Cite

show abstract

“…Ullah et al [7] conducted a rigorous analysis of the various image processing methods for brain tumor detection. There are many pre-processing techniques used like denoising, skull stripping, and intensity normalization.…”

Section: Literature Surveymentioning

confidence: 99%

Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques

Dewan¹,

Thepade²,

Deshmukh³

et al. 2023

IJETT

View full text Add to dashboard Cite

The detection of brain tumors using Magnetic Resonance Images(MRI) is difficult for contemporary medical imaging research. Basically, a brain tumor is an expansion of aberrant brain cells that expand erratically and seemingly uncontrolled. Meningioma, Glioma, and Pituitary are the three kinds of tumors that are most frequently seen. Early identification is essential for the successful treatment of brain tumors. With the development of medical imaging, doctors now employ a variety of imaging methods, such as fMRI, EEG, etc., to diagnose brain tumors. These imaging methods can help clinicians establish a precise diagnosis and create a treatment strategy by providing details on the location, size, and shape of brain tumors. Feature extraction and classification are two steps in the categorization of brain tumors. Two traditional manual feature extraction methods were frequently utilized in certain earlier research to extract details like the intensity and texture of images of brain tumors. This work employs the 'GLCM(Grey Level Co-occurrence Matrix)' approach for feature extraction. The generated feature set is provided to machine learning(ML) algorithms, including 'K-Nearest Neighbours(KNN), Support Vector Machine(SVM), Decision Tree(DT), Naive Bayes(NB), Logistic Regression(LR), Naive Bayes(NB), and Random Forest(RF)'. According to experimental results, random forest yields a maximum accuracy of 91.04%. The proposed methodology helps to classify the different brain tumor classes like glioma, meningioma, pituitary, or else no tumor.

show abstract

“…The goal of this image processing is always to determine tumor location [28]. The main concern of segmentation, detection, and extraction of tumor area from imaging modalities images that helps to perform radiologists or clinical experts for treatment planning [29].…”

Section: Tumor Localizationmentioning

confidence: 99%

Digital Image Processing and Its Application for Medical Physics and Biomedical Engineering Area

Karmaker¹

2022

Digital Image Processing Applications

View full text Add to dashboard Cite

The proper use of imaging modalities produces an image that aids in the detection of early stage abnormalities such as cancer, the identification of small precise lesions, and the presentation of internal illustration. A high-quality image can help doctors, radiologists, medical physicists, biomedical engineers, and scientists to make important decisions on ameliorate treatment planning that can reduce cancer mortality rates and provide life-saving results. This chapter outlines the features, attributes, and processing techniques of various medical imaging modalities utilized in the fields of radiation therapy and biomedical engineering. This study highlighted the significance of image processing in medical physics and biomedical engineering, characteristics of mammography, computed tomography (CT), ultrasound, magnetic resonance imaging (MRI), and positron emission tomography (PET) images. With their advanced application, various image processing approaches are distinguished. Images are collected through the journal, useful websites, the internet, or other sources. That can help teachers, students, researchers, scientists, and others comprehend and learn how to apply image processing techniques and which techniques will suit which modalities image. This chapter will provide a clear understanding of image processing techniques for medical physics and biomedical engineering participants, as well as an abundance of learning opportunities.

show abstract

Critical Analysis of Brain Magnetic Resonance Images Tumor Detection and Classification Techniques

Cited by 4 publications

References 58 publications

Computer-Aided Design on Image Detection: A Chronology Review

Computer-Aided Design on Image Detection: A Chronology Review

Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques

Digital Image Processing and Its Application for Medical Physics and Biomedical Engineering Area

Contact Info

Product

Resources

About