Semantic Segmentation of Brain Tumor from MRI Images and SVM Classification using GLCM Features

Hussain, Ashfaq; Khunteta, Ajay

doi:10.1109/icirca48905.2020.9183385

Cited by 44 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The threshold value of the probability map's combined grey and white matter has been used to perform skull stripping, but if the estimation and initialization are done incorrectly, poor results are obtained [17]. In 2020, Hussain and Khunteta [19] suggested a technique to isolate the tumor region from MRI images. followed by GLCM methods to extract the features.…”

Section: Related Workmentioning

confidence: 99%

Automatic Skull Stripping of MRI Head Images Based on Adaptive Gamma Transform

Dawood¹,

Hashim²,

Nasser³

2023

MMEP

View full text Add to dashboard Cite

Skull stripping is regarded as an important pre-processing step by many neuroimaging processing applications. An appropriate skull stripping is crucial because of the complex anatomical makeup of the brain and variations in brain MRI intensity. The removal of the skull region for clinical analysis in brain segmentation tasks is essentially the process of "skull stripping," and its accuracy and effectiveness are very important for diagnostic purposes. It is thought to be a difficult task because it calls for more precise and thorough methods for separating the different regions of the brain and the skull. Consequently, a technique is suggested for skull stripping by improving the contrast of the brain image using Adaptive gamma correction (AGC), which sets its settings dynamically based on the properties of the input image. In addition, the largest connected components, morphological image processing technique, and image multiplications are used in the proposed skull stripping method. The Br35H::Brain Tumor Detection 2020 dataset and Brain MRI Images for Brain Tumor Detection dataset have been used for the experimentation. The results of the experiments show that the proposed image enhancement and skull removal techniques work effectively with an accuracy rate of 96%.

show abstract

Section: Related Workmentioning

confidence: 99%

Automatic Skull Stripping of MRI Head Images Based on Adaptive Gamma Transform

Dawood¹,

Hashim²,

Nasser³

2023

MMEP

View full text Add to dashboard Cite

show abstract

“…Support Vector Machines (SVM) becomes one of the methods in supervised learning, typically applied for classification and regression [19]- [21]. SVM when compared with other classification methods offers a better concept in addressing either linear or non-linear problems.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine

Minarno

Kantomo²,

Sumadi³

et al. 2022

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.

show abstract

“…Penelitian sebelumnya tentang segmentasi area tumor otak menggunakan MRI, beberapa peneliti menggunakan metode segmentasi watershed, sehingga dapat diperoleh area tumor yang tersegmentasi [4]. Dalam penelitian ini menggunakan model AGResU-Net untuk segmentasi tumor otak MRI [10].…”

Section: Hasil Dan Pembahasanunclassified

Analisa gambar citra MRI otak dengan watershed dan ekstraksi fitur GLCM

Wulansari

Rahman

2022

JNNLK

View full text Add to dashboard Cite

Tumor otak merupakan infeksi berupa jaringan yang tidak diinginkan dan sangat membahayakan. Sangat sulit untuk membedakan jaringan tumor otak dari bagian otak lainnya. Deteksi dini tumor sangat penting untuk menyelamatkan nyawa pasien. Strategi segmentasi dapat digunakan untuk mengidentifikasi dan mengurai area tumor otak dengan citra Magnetic Resonance Imaging (MRI) otak. Hal ini merupakan terobosan yang penting untuk masa depan. Pencitraan resonansi magnetik merupakan bidang yang ekstrem dalam image processing karena tingkat presisi harus sangat tinggi sehingga dokter dapat memperoleh rekomendasi yang tepat tentang infeksi untuk menyelamatkan nyawa pasien. Citra MRI dapat digunakan untuk memberikan informasi pemisahan jaringan tumor otak. Segmentasi dari citra MRI dengan median filtering dan teknik preprocessing pengupasan tengkorak, threshold grip dengan watershed memperoleh hasil contrast 4,287, correlation 0,946, homogeneity 0,721, dan energy 0,278.

show abstract

Semantic Segmentation of Brain Tumor from MRI Images and SVM Classification using GLCM Features

Cited by 44 publications

References 13 publications

Automatic Skull Stripping of MRI Head Images Based on Adaptive Gamma Transform

Automatic Skull Stripping of MRI Head Images Based on Adaptive Gamma Transform

Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine

Analisa gambar citra MRI otak dengan watershed dan ekstraksi fitur GLCM

Contact Info

Product

Resources

About