An efficient brain tumor detection from MRI images using entropy measures

Somwanshi, Devendra; Kumar, Ashutosh; Sharma, Pratima; Joshi, Deepika

doi:10.1109/icraie.2016.7939554

Cited by 21 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their system consisted of four components: pre-processing, image segmentation, feature extraction, and image classification. Devendra Somwaanshi et al [4] presented an efficient mechanism for brain tumor detection from MRI images using entropy measures. They conducted a survey on different entropy functions for tumor segmentation and detection from various MRI images.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks

S. M.,

B. K.

2023

IJRITCC

View full text Add to dashboard Cite

The brain, as the central nervous system's most critical part, can develop abnormal growths of cells known as tumors. Cancer is the term used to describe malignant tumors. Medical imaging modalities, such as computed tomography (CT) or magnetic resonance imaging (MRI), are commonly used to detect cancerous regions in the brain. Other techniques, such as positron emission tomography (PET), cerebral arteriography, lumbar puncture, and molecular testing, are also utilized for brain tumor detection. MRI scans provide detailed information concerning delicate tissue, which aids in diagnosing brain tumors. MRI scan images are analyzed to assess the disease condition objectively. The proposed system aims to identify abnormal brain images from MRI scans accurately. The segmented mask can estimate the tumor's density, which is helpful in therapy. Deep learning techniques are employed to automatically extract features and detect abnormalities from MRI images. The proposed system utilizes a convolutional neural network (CNN), a popular deep learning technique, to analyze MRI images and identify abnormal brain scans with high accuracy. The system's training process involves feeding the CNN with large datasets of normal and abnormal MRI images to learn how to differentiate between the two. During testing, the system classifies MRI images as either normal or abnormal based on the learned features. The system's ability to accurately identify abnormal brain scans can aid medical practitioners in making informed decisions and providing better patient care. Additionally, the system's ability to estimate tumor density from the segmented mask provides additional information to guide therapy. The proposed system offers a promising solution for improving the accuracy and efficiency of brain tumor detection from MRI images, which is critical for early detection and treatment.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks

S. M.,

B. K.

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…Similarly it had a drawback as latency and security in segmentation. Bandy and Mir [13] proposed semiautomatic technique to segment MR brain images. For the framework of segmented regions the feature map was introduced.…”

Section: Related Workmentioning

confidence: 99%

Active Contour Model for Brain MR Tumor Segmentation and Volume Estimation

Kumar*,

Sridevi

2019

IJEAT

View full text Add to dashboard Cite

Brain MR tumor segmentation and estimation of volume is a critical task in medical applications. Brain tumors are analyzed by the common test method known as magnetic resonance imaging (MRI) which provides a detail image of brain. The proposed work involves detection of tumor in brain using deep learning based active contour model. Segmentation is the main objective of the proposed work for achieving detailed information about the tumor and accurate volume estimation to detect the size of the tumor. The Euclidean similarity factor (ESF) is used for considering the spatial distances and intensity differences of the region there by preserving all the fine details of the image. 3D convolutional neural network (3DCNN) is used for extracting the features and segmentation to identify the tumor location in the brain. Finally, shoelace method is used to estimate the volume of the tumor, and it provides treatment planning, surgical methods, estimation of dose, etc. The simulation results in this suggested approach could attain effective performance as compared with the existing approaches.

show abstract

“…Salwe et al [23] have introduced an adaptive process of wavelet implementation where fine windowing operation as well as thresholds was used for detecting tumor cells of brain. Adoption of entropy factor toward similar detection problem was seen in the work carried out by Somwanshi et al [24] for enhancing the segmentation performance. Dictionary learning was used adopted for incorporating adaptiveness in the detection process followed by usage of sparse reconstruction process as seen in the work carried out by Su et al [25].…”

Section: Introductionmentioning

confidence: 99%

A novel framework for efficient identification of brain cancer region from brain MRI

Angadi¹,

Nagendra²,

Hanumanthappa³

2019

IJECE

View full text Add to dashboard Cite

Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.

show abstract

An efficient brain tumor detection from MRI images using entropy measures

Cited by 21 publications

References 6 publications

Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks

Automated Brain Tumor Detection from MRI Scans using Deep Convolutional Neural Networks

Active Contour Model for Brain MR Tumor Segmentation and Volume Estimation

A novel framework for efficient identification of brain cancer region from brain MRI

Contact Info

Product

Resources

About