A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

2020 ◽  
Vol 17 (4) ◽  
pp. 1925-1930
Author(s):  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
Robbi Rahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Convolution Algorithm ◽  
The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

scholarly journals A Review Article on Brain Tumor Detection and Optimization using Hybrid Classification Algorithm

2021 ◽  
Vol 9 (11) ◽  
pp. 763-768
Author(s):  
Nitesh Yadav
Keyword(s):  
Brain Tumor ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Review Article ◽  
Processing Method ◽  
Computational Results ◽  
X Ray ◽  
Hybrid Classification ◽  
The Brain

Abstract: This review focuses on different imaging techniques such as MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and timeconsuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Keywords: Brain tumor, data mining techniques, filtering techniques, MRI, classifiers, feature selection.

scholarly journals A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 518 ◽  
Author(s):  
Hafsa Khalid ◽  
Muzammil Hussain ◽  
Mohammed A. Al Ghamdi ◽  
Tayyaba Khalid ◽  
Khadija Khalid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Ct Scan ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
Learning Techniques

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

scholarly journals Fusion of brain MRI and CT images and Classification of brain tumor using Machine Learning Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 1699-1703
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Image Fusion ◽  
Ct Scan ◽  
Ct Images ◽  
Accurate Information ◽  
Individual Source ◽  
The Individual ◽  
The Brain

Brain tumor is the most common and destructive disease which reduces the life time of people. The earlier detection of brain tumor plays a most important role for better treatment of the patient. In this paper, a new technique for brain tumor classification using machine learning by fusion of MRI and CT images are proposed. Image fusion is a process of fusing two or more images (i.e. MRI and CT scan images) to obtain a new one which contains more accurate information of the brain than any of the individual source images. Initially fusion of MRI and CT scan images has been carried out using Stationary Wavelet Transform (SWT). Then watershed transform is applied for image segmentation and discriminative robust local binary patter (DRLBP)is employed to extract the features exactly from the fused image. Classification of the tumor is done by Support Vector Machine (SVM) thereby reducing the generalization error and increasing the accuracy. The ultimate goal is to classify the tissues into normal and abnormal using machine learning algorithms .Image fusion process yields more accurate information of the brain than any of the individual source images.

scholarly journals Image Based Group Classifier for Brain Tumor Detection Using Machine Learning Technique

2020 ◽  
Vol 37 (5) ◽  
pp. 865-871
Author(s):  
Putta Rama Krishnaveni ◽  
Gattim Naveen Kishore
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
The United States ◽  
Tumor Detection ◽  
Tumor Type ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
The Brain

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

scholarly journals Normal And Abnormal Detection For Knee Osteoarthritis Using Machine Learning Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 6026-6033 ◽  
Keyword(s):  
Machine Learning ◽  
Knee Joint ◽  
Imaging Techniques ◽  
Joint Space ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Abnormal Detection ◽  
X Ray ◽  
Knee Oa ◽  
Computer Aided

Osteoarthritis is the most broadly recognized disease in the knee joint that affects the cartilage, especially among the old age or overweight people. In the normal knee joint, the smooth and thin layer called cartilage covers the joint space of the bone and makes the joint smooth and prevents them from rubbing against one another, but can break, when the cartilage gets ruptured due to which bones start rubbing with one another, and this may cause severe pain, swelling and stiffness in the knee joint. The evaluation for osteoarthritis detection includes a clinical examination, and different medical imaging techniques are X-RAY images and MRI scans. There is developing method required for classification frameworks that can precisely distinguish and identify knee OA from plain radiographs. In this method we have examining the strategy of computer aided diagnosis for early identification of knee OA. Based on the procedure of x rays through computer image processing, segmentation, feature extraction and investigation by means of building a classifier, a viable computer aided detection method for knee was made to help specialists in their precise, convenient and identification of potential risk of OA. For this method a total of 126 knee x ray image were collected for assessing the knee OA. In this paper, we tried to diagnose about the normal or abnormal detection of cartilage depreciation. The HOG and DWT features are extracted from X-ray images of the knee joints. The extracted features are classified with two different machine learning classifiers, namely the SVM and ANN Patternet classifiers, and the results are demonstrated. The SVM classification is good when compared with ANN and provides a satisfactory accuracy rate of 85.33%. At last the classifier was superior both in time effectiveness and classification execution to the regularly utilized classifiers based on iterative learning. In this way it was suitable to utilize as a computer aided tool for the diagnosis of OA.

scholarly journals Localization and Classification of Brain Tumor using Machine Learning & Deep Learning Techniques

2019 ◽  
Vol 8 (9S) ◽  
pp. 59-66
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
State Of The Art ◽  
Body Part ◽  
X Rays ◽  
Meaningful Information ◽  
Learning Techniques ◽  
The Brain

Digital image processing is a rising field for the investigation of complicated diseases such as brain tumor, breast cancer, kidney stones, lung cancer, ovarian cancer, and cervix cancer and so on. The recognition of the brain tumor is considered to be a very critical task. A number of approaches are used for the scanning of a particular body part like CT scan, X-rays, and Magnetic Resonance Image (MRI). These pictures are then examined by the surgeons for the removal of the problem. The main objective of examining these MRI images (mainly) is to extract the meaningful information with high accuracy. Machine Learning and Deep Learning algorithms are mainly used for analysing the medical images which can identify, localize and classify the brain tumor into sub categories, according to which the diagnosis would be done by the professionals. In this paper, we have discussed the different techniques that are used for tumor pre-processing, segmentation, localization, extraction of features and classification and summarize more than 30 contributions to this field. Also, we discussed the existing state-of-the-art, literature gaps, open challenges and future scope in this area.

scholarly journals Exploring Statistical Parameters of Machine Learning Techniques for Detection and Classification of Brain Tumor

2019 ◽  
Vol 8 (10) ◽  
pp. 4118-4124
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Random Forest ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Computerized System ◽  
The Brain

A computerized system can improve the disease identifying abilities of doctor and also reduce the time needed for the identification and decision-making in healthcare. Gliomas are the brain tumors that can be labeled as Benign (non- cancerous) or Malignant (cancerous) tumor. Hence, the different stages of the tumor are extremely important for identification of appropriate medication. In this paper, a system has been proposed to detect brain tumor of different stages by MR images. The proposed system uses Fuzzy C-Mean (FCM) as a clustering technique for better outcome. The main focus in this paper is to refine the required features in two steps with the help of Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA) using three machine learning techniques i.e. Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The final outcome of our experiment indicated that the proposed computerized system identifies the brain tumor using RF, ANN and SVM with 100%, 91.6% and 95.8%, accuracy respectively. We have also calculated Sensitivity, Specificity, Matthews’s Correlation Coefficient and AUC-ROC curve. Random forest shows the highest accuracy as compared to Support Vector Machine and Artificial Neural Networks.

scholarly journals Algorithm for Brain Tumor Detection

2019 ◽  
Vol 8 (11S) ◽  
pp. 1035-1042
Keyword(s):  
Brain Tumor ◽  
Critical Role ◽  
Tumor Detection ◽  
Test Case ◽  
Brain Scans ◽  
Detection Techniques ◽  
Segmentation Algorithms ◽  
Processing Techniques ◽  
Compare And Contrast ◽  
The Brain

In today’s world many engineers have been concentrating in developing various tools for detection of tumor and processing its medical images. The extraction of brain tumor and analysing it is a very challenging task in the field of healthcare. Segmentation’s introduction solves the complexity to medical imaging and in turn “MRI (magnetic resonance imaging)” proves to bea very useful diagnostic tool for the detection of brain tumorin MRI’s. Here we have performed a comparative study between various clustering and segmentation algorithms. In healthcare field, detection of brain tumor from MRI of the brain, is the current most favourable and seeded area of research. Detecting tumors is one of the major focus areas of the system, it plays a critical role in extraction of details from graphic generated contents of the healthcare. MRI’s with brain scans are used in the processes. We have implemented “k-means, fuzzy-c means and watershed segmentation”with various soft computing image processing techniques in various test case scenarios which allows us to compare and contrast between the stated techniques. This paper also focuses on enhancing the performance of the algorithms by setting up a suitable parallel environment for these three tumor detection techniques. This will allow multiple MRI’s being evaluated simultaneously.

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