Combined Features in Region of Interest for Brain Tumor Segmentation

Alqazzaz, Salma; Sun, Xianfang; Nokes, Leonard Derek Martin; Yang, Hong; Yang, Yingxia; Xu, Ronghua; Zhang, Yanqiang; Yang, Xin

doi:10.1007/s10278-022-00602-1

Cited by 8 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This meningioma detection framework was compared with conventional tumor segmentation methods, as shown in Table 13 , with respect to the brain MRI images in the Nanfang dataset. As shown in Table 14 , the proposed HCNN technique produces the best simulation results when compared with conventional methods 27 , 29 – 34 .…”

Section: Resultsmentioning

confidence: 96%

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Prakash,

Kannan,

Santhiyakumari

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study.

show abstract

Section: Resultsmentioning

confidence: 96%

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Prakash,

Kannan,

Santhiyakumari

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In supervised machine learning, a training data set consisting of MRI images and corresponding manually segmented labels is used to train a machine learning model. Various algorithms, such as decision tree, 33 random forests, 34,35 and SVM [36][37][38][39][40][41] can be utilized. Machine learning models learn to classify and segment different regions using the features derived from the input images.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Machine learning and deep learning for brain tumor MRI image segmentation

Khan,

Guo,

Liu

et al. 2023

Exp Biol Med (Maywood)

View full text Add to dashboard Cite

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

show abstract

“…In addition to being a lightweight module that can easily adjust BTS models, the 3D SGE module can learn sub-features and reduce noise in a targeted manner for each group. DL is unable to give the necessary local features related to changes in tissue texture brought on by tumor growth; to address this issue Salma Alqazzaz et al [74] proposed an approach in which features are combined in region of interest (ROI) for BTS. In this approach creates a hybrid technique that combines hand-crafted and ML features.…”

Section: Recent Approaches Of Brain Neoplasm Segmentation Techniquesmentioning

confidence: 99%

Recent Segmentation Trends of MRI-Based Brain Neoplasm: A Review

Siddiqui,

Qadri,

Tayyab

et al. 2023

JAET

View full text Add to dashboard Cite

A significant increase in medical-related cases of brain neoplasm has been noticed in the past few years affecting not only adults but children as well. Brain neoplasm segmentation isolated the different abnormal brain tissues from normal brain tissues which are complicated tasks in medical image examination. But simultaneously it plays a crucial role in time diagnosis which not only improved the treatment possibilities but also increased the rate of survival of the patients because a brain neoplasm is a treatable kind of cancer if diagnose well on time. Magnetic resonance imaging (MRI) based segmentation are a more focused, attractive, and attentive study area in recent few years because MRI is noninvasive imaging, safe, and cost-effective. Detection of brain neoplasm using the manual procedure of segmentation is an extremely difficult, time-consuming, expensive, and individual task because large data of MRI images are produced in clinical practice which may delay the diagnosis. This increases the practical significance of the automatic segmentation techniques but due to brain neoplasm being extremely unpredictable concerning position, appearance, type, and size improving the methods for state-of-the-art segmentation process remain a complex task. To segment the brain neoplasm accurately, automatic segmentation is a solution with better performance. The goal of this paper provides an organized literature survey for recent MRI-based automatic brain neoplasm segmentation techniques with the modern participation of several researchers which helps new researchers in exploring future directions. There are several current surveys focused on traditional methods, but this paper focuses on recent trends including machine learning techniques accompanied by transfer learning, deep learning, neural network, and hybridization. Moreover, this survey also presents the findings and limitations of each article which show the effectiveness of the proposed work. Finally, this survey, found that after over two decades of research, the novel methods for segmenting brain neoplasms using computer-aided techniques are becoming more and more refined and becoming closer to being used often in clinical settings but in terms of computing complexity and memory consumption, these approaches lag.

show abstract

Combined Features in Region of Interest for Brain Tumor Segmentation

Cited by 8 publications

References 29 publications

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Machine learning and deep learning for brain tumor MRI image segmentation

Recent Segmentation Trends of MRI-Based Brain Neoplasm: A Review

Contact Info

Product

Resources

About