scholarly journals Accurate Brain Tumor Recognition Using Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection

2021 ◽  
Vol 38 (5) ◽  
pp. 1377-1383
Author(s):  
Revathi Vankayalapati ◽  
Akka Lakshmi Muddana
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Survival Rates ◽  
Prediction Method ◽  
Tumor Detection ◽  
Brain Images ◽  
Tumor Recognition

In clinical practice and patient survival rates, early diagnosis of brain tumors plays a key role. Different forms of brain tumors and their properties and treatments are available. Therefore, tumor detection is complicated, time consuming and error-prone with manual brain tumor detection. Therefore, high-precision automated, computerized diagnostics are currently necessary. Feature extraction is a tumor prediction method for capturing the visual content of a picture. The extraction of features is the process through which the raw image is reduced and decisions like the pattern classification are facilitated. The MRI brain images are considered to be classified as a robust and more accurate classification that is able to serve as an expert assistant for healthcare practitioners. In this research, a new method for selecting and extracting features is introduced. The paper proposes to take into account the most important features for the classification of tumor and non-tumor cells using a Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection (DWLM-PWFS). This approach combines the tumor's intensity, texture, shape and diagnostic properties. The selection of features with the technique proposed is most helpful for analyzing data according to grouping class variable and ensuring reduced feature setting with high classification accuracy. In contrast to the conventional model, the model proposed is shown to be highly efficient in comparison with traditional models.

scholarly journals Brain Tumor Detection and Segmentation: A Survey

2020 ◽  
Vol 10 (4) ◽  
pp. 55
Author(s):  
Kalifa Shantta ◽  
Otman Basir
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Survival Rates ◽  
Automatic Detection ◽  
Tumor Detection ◽  
Detection Techniques ◽  
Pros And Cons ◽  
Accuracy Performance ◽  
Image Modality

<p class="Abstract">Even with the enormous progress in medical technology, brain tumor detection is still an extremely tedious and complex task for the physicians. The early and accurate detection of brain tumors enables effective and efficient therapy and thus can result in increased survival rates. Automatic detection and classification of brain tumors have the potential to achieve efficiency and a higher degree of predictable accuracy. However, it is well established that the accuracy performance of automatic detection and classification techniques varies from technique to technique, and tends to be image modality dependent. This paper reviews the state-of-the-art detection techniques and highlights their pros and cons.</p>

scholarly journals Brain Tumor Detection using Enhanced Convolution Neural Network for MR Images

2021 ◽  
Vol 10 (2) ◽  
pp. 10-13
Author(s):  
M.B. Bramarambika ◽  
◽  
M Sesha Shayee ◽  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Detection System ◽  
Human Life ◽  
Classification Problem ◽  
Tumor Detection ◽  
Convolution Neural Network ◽  
Brain Images ◽  
The Brain

Brain tumor is a mass that grows unevenly in the brain and directly affects human life. The mass occurs spontaneously because of the tissues surrounding the brain or the skull. There are two types of Brain tumor such as Benign and Malignant. Malignant brain tumors contain cancer cells and grow quickly and spread through to other brain and spine regions as well. Accurate and prompt diagnosis of brain tumors is essential for implementing an effective treatment of this disease. Brain images produced by the Magnetic Resonance Imaging (MRI) technique are a rich source of data for brain tumor diagnosis and treatment in the medical field. Due to the existence of a large number of features compared to the other imaging types. The performance of existing methods is inadequate considering the medical significance of the classification problem. Earlier methods relied on manually delineated tumor regions, prior to classification. This prevented them from being fully automated. The automatic algorithms developed using CNN and its variants could not achieve an influential improvement in performance. In order to overcome such an issue, the proposed one is automatic brain tumor detection system, which is “ Enhanced Convolution Neural Network (CNN) Algorithm for MRI Images” for the detection of brain tumor is useful to detect and classify the Glioma part into low Glioma and high Glioma.

scholarly journals A Review on Brain Tumor Classification Methodologies

2019 ◽  
pp. 346-359 ◽  
Author(s):  
Bichitra Panda ◽  
Chandra Sekhar Panda
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Classification ◽  
Low Grade ◽  
High Grade ◽  
Mr Images ◽  
The Brain

Brain tumor is one of the leading disease in the world. So automated identification and classification of tumors are important for diagnosis. Magnetic resonance imaging (MRI)is widely used modality for imaging brain. Brain tumor classification refers to classify the brain MR images as normal or abnormal, benign or malignant, low grade or high grade or types. This paper reviews various techniques used for the classification of brain tumors from MR images. Brain tumor classification can be divided into three phases as preprocessing, feature extraction and classification. As segmentation is not mandatory for classification, hence resides in the first phase. The feature extraction phase also contains feature reduction. DWT is efficient for both preprocessing and feature extraction. Texture analysis based on GLCM gives better features for classification where PCA reduces the feature vector maintaining the accuracy of classification of brain MRI. Shape features are important where segmentation has already been performed. The use of SVM along with appropriate kernel techniques can help in classifying the brain tumors from MRI. High accuracy has been achieved to classify brain MRI as normal or abnormal, benign or malignant and low grade or high grade. But classifying the tumors into more particular types is more challenging.

scholarly journals Brain Tumor Detection Based on Features Extracted and Classified Using a Low-Complexity Neural Network

2021 ◽  
Vol 38 (3) ◽  
pp. 547-554
Author(s):  
Vasileios Papageorgiou
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Low Complexity ◽  
Automatic Classification ◽  
Tumor Detection ◽  
Cross Entropy ◽  
Testing Accuracy ◽  
Jensen Shannon Divergence

Brain tumor detection or brain tumor classification is one of the most challenging problems in modern medicine, where patients suffering from benign or malignant brain tumors are usually characterized by low life expectancy making the necessity of a punctual and accurate diagnosis mandatory. However, even today, this kind of diagnosis is based on manual classification of magnetic resonance imaging (MRI), culminating in inaccurate conclusions especially when they derive from inexperienced doctors. Hence, trusted, automatic classification schemes are essential for the reduction of humans’ death rate due to this major chronic disease. In this article, we propose an automatic classification tool, using a computationally economic convolutional neural network (CNN), for the purposes of a binary problem concerning MRI images depicting the existence or the absence of brain tumors. The proposed model is based on a dataset containing real MRI images of both classes with nearly perfect validation-testing accuracy and low computational complexity, resulting a very fast and reliable training-validation process. During our analysis we compare the diagnostic capacity of three alternative loss functions, validating the appropriateness of cross entropy function, while underlining the capability of an alternative loss function named Jensen-Shannon divergence since our model accomplished nearly excellent testing accuracy, as with cross-entropy. The multiple validation tests applied, enhancing the robustness of the produced results, render this low-complexity CNN structure as an ideal and trustworthy medical aid for the classification of small datasets.

scholarly journals AUTOMATIC SEGMENTATION AND CLASSIFICATION OF BRAIN TUMOR USING DEEP LEARNING

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
K.Ganga Durga Prasad ◽  
A.J.N. Murthy ◽  
G Narasimha ◽  
New Sinha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Tumor Detection ◽  
Tumor Type ◽  
The Brain

The brain tumors, are the maximum not unusual place and threatening disease, main to a totally quick lifestyles of their maximum grade. Thus, remedy making plans is a key level to enhance the lifestyles of sufferers. Normally, distinct photo strategies which includes CT, MRI and ultrasound photo are used to hit upon the tumor in a brain. on this approach MRI photos are used to diagnose brain tumor guide type of tumor vs non-tumor is a tough challenge for radiologosts. we gift an approach for detection and type of tumors with inside the brain. The computerized brain tumor type could be very hard challenge in brain tumor. In this approach, computerized brain tumor detection is executedwith the aid of usingthe use of Convolutional Neural Networks (CNN) type.Our proposed automation gadgetcould take an MRI and examine it to locate bengin (non-cancerous) or malignant (cancerous).

scholarly journals Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

2021 ◽  
Vol 5 (1) ◽  
pp. 21
Author(s):  
Krisna Nuresa Qodri ◽  
Indah Soesanti ◽  
Hanung Adi Nugroho
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Transfer Learning ◽  
Medical Personnel ◽  
The United States ◽  
Brain Images ◽  
Training And Research ◽  
The Brain

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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