An improved brain tumor detection and classification mechanism

Shil, Shubhashis Kumar; Polly, F. P.; Hossain, Mohammad Arif; Ifthekhar, Md. Shareef; Uddin, Mohammed Nazim; Jang, Yeong Min

doi:10.1109/ictc.2017.8190941

Cited by 27 publications

(12 citation statements)

References 6 publications

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“…Consider how the IoMT promotes equal 24/7 access to patient and physician. is is one of the few things that IoMT can do by developing established best practices [6]. Another important way IoMT works in the medical field is to help provide greater flexibility for physicians and other medical professionals [7].…”

Section: Introductionmentioning

confidence: 99%

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Sriram

Reddy

Babu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients’ health report and provides better treatment.

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Section: Introductionmentioning

confidence: 99%

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Sriram

Reddy

Babu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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“…The TPR measure of this method for LGG reached 83%, and the accuracy measure for HGG reached 91%. Sher et al [73] first segmented the image by the Otsu method and K-means clustering, then extracted the features by discrete wavelet transformation, and finally reduced the feature dimension by the PCA algorithm to obtain the best features for SVM classification. The experimental results show that the sensitivity and specificity of the scheme can reach more than 90%.…”

Section: Segmentation Methods Of Brain Tumor Mr Images Based On Traditional Machine Learningmentioning

confidence: 99%

Overview of Multi-Modal Brain Tumor MR Image Segmentation

Zhang

Yang³

et al. 2021

Healthcare

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The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

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“…However, Brain tumour classification occupies many studies to decide the infected brain, which substantial for any research interested in class classification. For this purpose, several classifiers can be used, such as Kernel Support Vector Machine (KSVM) [3,5]. The KSVM classifier is an efficient technique that provides an accurate forecast and classification.…”

Section: Introductionmentioning

confidence: 99%

“…The KSVM classifier is an efficient technique that provides an accurate forecast and classification. The KSVM classifier can classify images into normal and abnormal [5,6] or benign and malignant tumours [7,8] It requires specific training of the extracted features which are necessary for any image-based applications [9]. The feature extraction can be achieved by a Discrete Wavelet Transform (DWT) [10] or by the shape analysis method to retrieve and recognize the objects represented in the images [11].…”

Section: Introductionmentioning

confidence: 99%

A combining two ksvm classifiers based on True pixel values and Discrete wavelet transform for mri-based brain tumor detection and Classification

M¹,

Noaman²,

Azzawi³

2022

ETJ

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 We have proposed an integrated algorithm to classify brain tumors following two stages.  We have used the Kernel Support Vector Machine (KSVM) classifier.  The linear kernel achieved 97.5% accuracy and 98.57% accuracy in the first and second classifiers. The studies on brain tumor detection and classification are continuing to improve the specialists' ability in diagnosis. Magnetic Resonance Imaging (MRI) is one of the most common techniques used to evaluate brain tumors diagnosis. However, brain tumors diagnosis is a difficult process due to congenital malformations and possible errors in diagnosing benign from malignant tumors. Therefore, this research aims to propose an integrated algorithm to classify brain tumors following two stages using the Kernel Support Vector Machine (KSVM) classifier. First stage classifies the tumors as normal and abnormal, and the second classifies abnormal tumors as benign and malignant. The first KSVM employs extraction features by considering the pixel values to classify images as a shape. In contrast, the second KSVM uses the Discrete Wavelet Transform (DWT), followed by the Principal Component Analysis (PCA) technique to extract and reduce features and improve the model performance. Also, K-means clustering algorithm is used to segment, isolate and calculate the tumor area. The KSVM classifiers use two kernels (linear and Radial Basis Function (RBF)).Obtained results showed that the linear kernel achieved 97.5% accuracy and 98.57% accuracy in the first and second classifier, respectively. For all linear classifiers, a 100% sensitivity level is achieved. This work validates the proposed model based on the (K-fold) strategy.

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An improved brain tumor detection and classification mechanism

Cited by 27 publications

References 6 publications

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Overview of Multi-Modal Brain Tumor MR Image Segmentation

A combining two ksvm classifiers based on True pixel values and Discrete wavelet transform for mri-based brain tumor detection and Classification

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