Classification Of Brain Images Research Articles

Multi-class brain tumor classification using residual network and global average pooling

A rapid increase in brain tumor cases mandates researchers for the automation of brain tumor detection and diagnosis. Multi-tumor brain image classification became a contemporary research task due to the diverse characteristics of tumors. Recently, deep neural networks are commonly used for medical image classification to assist neurologists. Vanishing gradient problem and overfitting are the demerits of the deep networks. In this paper, we have proposed a deep network model that uses ResNet-50 and global average pooling to resolve the vanishing gradient and overfitting problems. To evaluate the efficiency of the proposed model simulation has been carried out using a three-tumor brain magnetic resonance image dataset consisting of 3064 images. Key performance metrics have used to analyze the performance of the proposed model and its competitive models. We have achieved a mean accuracy of 97.08% and 97.48% with data augmentation and without data augmentation, respectively. Our proposed model outperforms existing models in classification accuracy.

Hybridization Techniques To Detect Brain Tumor

Diagnosing brain tumor in present era through digital techniques need serious attention as the number of patients are increasing in an awkward manner. Magnetic Resonance Imaging is the tool that is used for detection of brain tumors. This paper is classified in two phases i.e. normal and abnormal brain images. Then, Feature selection and classification are applied on the given data set. Classification on given data set is done through K- Nearest Neighbor. In the given study, we have taken normal and abnormal samples from Nishtar Medical hospital, Multan. In order to classify brain images, first it needs to pre-process through skull stripping technique then the proposed algorithm is followed. Algorithm involves feature extraction through GLCM and feature selection through ACO. Results have proved its efficiency level up-to 88%.

An Efficient Classification of MRI Brain Images

The unprecedented improvements in computing capabilities and the introduction of advanced techniques for the analysis, interpretation, processing, and visualization of images have greatly diversified the domain of medical sciences and resulted in the field of medical imaging. The Magnetic Resonance Imaging (MRI), an advanced imaging technique, is capable of producing high quality images of the human body including the brain for diagnosis purposes. This paper proposes a simple but efficient solution for the classification of MRI brain images into normal, and abnormal images containing disorders and injuries. It uses images with brain tumor, acute stroke and alzheimer, besides normal images, from the public dataset developed by harvard medical school, for evaluation purposes. The proposed model is a four step process, in which the steps are named: 1). Pre-processing, 2). Features Extraction, 3). Features Reduction, and 4). Classification. Median filter, being one of the best algorithms, is used for the removal of noise such as salt and pepper, and unwanted components such as scalp and skull, in the pre-processing step. During this stage, the images are converted from gray scale to colored images for further processing. In second step, it uses Discrete Wavelet Transform (DWT) technique to extract different features from the images. In third stage, Color Moments (CMs) are used to reduce the number of features and get an optimal set of characteristics. Images with the optimal set of features are passed to different classifiers for the classification of images. The Feed Forward - ANN (FF-ANN), an individual classifier, which was given a 65% to 35% split ratio for training and testing, and hybrid classifiers called: Random Subspace with Random Forest (RSwithRF) and Random Subspace with Bayesian Network (RSwithBN), which used 10-Fold cross validation technique, resulted in 95.83%, 97.14% and 95.71% accurate classification, in corresponding order. These promising results show that the proposed method is robust and efficient, in comparison with, existing classification methods in terms of accuracy with smaller number of optimal features.

Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks

In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.

A System for Brain Image Segmentation and Classification Based on Three-Dimensional Convolutional Neural Network

We consider the problem of fully automatic brain tumor segmentation in MR images containing glioblastomas. We propose a three-Dimensional Convolutional Neural Network (3D-CNN) approach that achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D-Brain CNN is formed directly on raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascading architecture with two pathways that each model normal details in tumors. Fully exploiting the convolutional nature of our model also allows us to segment a complete cerebral image in one minute. In experiments on the 2013 and 2015 BRATS challenge dataset; we exhibit that our approach is among the most powerful methods in the literature, while also being very effective.

Visual attention as a model for interpretable neuroimage classification in dementia

AbstractBackgroundDeep learning has the potential to aid clinical decision‐making in dementia, by automatically classifying brain images. However, several key limitations currently prohibit clinical adoption: 1) network design must be optimised for 3D neuroimaging; 2) analysis must be computationally feasible; 3) model decisions must be interpretable. Interpretability is particularly crucial, as clinicians need to understand how and why each automated decision is made.MethodWe address these issues using a 3D recurrent visual attention model tailored for neuroimaging: NEURO‐DRAM. The model comprises an agent which, trained by reinforcement learning, learns to navigate through volumetric images, selectively attending to the most informative regions for a given task. We trained and tested NEURO‐DRAM using T1‐weighted MRIs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This entailed n=162 Alzheimer’s Disease (AD) patients and n=160 healthy controls (HCs), split into training (90%) and testing (10%) data. Classification generalisability was evaluated using independent AD patients (n=130) and HCs (n=100) data from the Open Access Series of Imaging Studies (OASIS) dataset. Finally, we assessed the potential to transfer the classification task (i.e., no extra training needed) to discriminate between the baseline MRIs of people with stable or progressive mild cognitive impairment (MCI).ResultNEURO‐DRAM achieved 98.5% balanced accuracy when classifying AD patients from HCs from ADNI and 99.8% in OASIS, significantly out‐performing a baseline convolutional neural network. When classifying stable versus progressive MCI, accuracy was 77.8%. For each test participant, an individualised trajectory was obtained, depicting the brain regions that were used to make the specific classification (Fig. 1). The regions ‘visualised’ by the model’s trajectories included the hippocampus, parahippocampal gyrus and lateral ventricles. Computation time for training NEURO‐DRAM was substantially faster than the baseline network (10 minutes versus 45 minutes).ConclusionUsing a data‐driven approach, near‐perfect classification of AD patients from HCs can be achieved. To reach this high level of performance, our model learns to ‘visually’ attend to the areas of the brain radiologically associated with AD. Importantly, the neuroanatomical trajectory for each individual run through the analysis can be visualised, providing an intuitive way to interpret how NEURO‐DRAM has reached a classification decision.

Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI.

The projected burden of dementia by Alzheimer's disease (AD) represents a looming healthcare crisis as the population of most countries grows older. Although there is currently no cure, it is possible to treat symptoms of dementia. Early diagnosis is paramount to the development and success of interventions, and neuroimaging represents one of the most promising areas for early detection of AD. We aimed to deploy advanced deep learning methods to determine whether they can extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. We tailored and trained Convolutional Neural Networks (CNNs) on sMRIs of the brain from datasets available in online databases. Our proposed method, ADNet, was evaluated on the CADDementia challenge and outperformed several approaches in the prior art. The method's configuration with machine-learning domain adaptation, ADNet-DA, reached 52.3% accuracy. Contributions of our study include devising a deep learning system that is entirely automatic and comparatively fast, presenting competitive results without using any patient's domain-specific knowledge about the disease. We were able to implement an end-to-end CNN system to classify subjects into AD, MCI, or CN groups, reflecting the identification of distinctive elements in brain images. In this context, our system represents a promising tool in finding biomarkers to help with the diagnosis of AD and, eventually, many other diseases.

Brain Image Analysis, Classification and Reconstruction

Decoding brain activity occurs in response to an external stimulus is a great problem and the first step to analysis, classification, reconstruction of the external stimulus which will lead to some marvelous application like reconstructing the visual images from the mind of a person in comma or identifying an object in the head of someone that has some psychological problem in the medical field, and criminal image reconstruction instead of forensic artists in security problems or instant design in marketing, etc... The problem with the brain data analysis is that the brain data like fMRI which we used here is very complicated. also acquiring the data is difficult and requires a lot of resources and time and this is why the fMRI datasets size is small. The data is divided into 3 subjects and each subject data consists of fMRI responses to 3 types of images(letters,artificial,natural). In this work, we focused on 3 main targets analysis and classification of the difference between subjects and types of images, identification of objects presented in the natural images, and finally reconstruction of the images from fMRI data.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

AbstractWe analyzed a data set containing functional brain images from 6 healthy controls and 196 individuals with Parkinson's disease (PD), who were divided into five stages according to illness severity. The goal was to predict patients' PD illness stages by using their functional brain images. We employed the following prediction approaches: multivariate statistical methods (linear discriminant analysis, support vector machine, decision tree, and multilayer perceptron [MLP]), ensemble learning models (random forest [RF] and adaptive boosting), and deep convolutional neural network (CNN). For statistical and ensemble models, various feature extraction approaches (principal component analysis [PCA], multilinear PCA, intensity summary statistics [IStat], and Laws' texture energy measure) were employed to extract features, the synthetic minority over‐sampling technique was used to address imbalanced data, and the optimal combination of hyperparameters was found using a grid search. For CNN modeling, we applied an image augmentation technique to increase and balance data sizes over different disease stages. We adopted transfer learning to incorporate pretrained VGG16 weights and architecture into the model fitting, and we also tested a state‐of‐the‐art machine learning model that could automatically generate an optimal neural architecture. We found that IStat consistently outperformed other feature extraction approaches. MLP and RF were the analytic approaches with the highest prediction accuracy rate for multivariate statistical and ensemble learning models, respectively. Overall, the deep CNN model with pretrained VGG16 weights and architecture outperformed other approaches; it captured critical features from imaging, effectively distinguished between normal controls and patients with PD, and achieved the highest classification accuracy.

RETRACTED ARTICLE: Brain image classification by the combination of different wavelet transforms and support vector machine classification

The human brain is the primary organ, and it is located in the centre of the nervous system in the human body. The abnormal cells in the brain are known as a brain tumor. The tumor in the brain does not spread to the other parts of the human body. Early diagnosis of brain tumor is required. In this work, an efficient technique is presented for magnetic resonance imaging (MRI) brain image classification using different wavelet transforms like discrete wavelet transform (DWT), stationary wavelet transform (SWT) and dual tree M-band wavelet transform (DMWT) for feature extraction and selection of coefficients and support vector machine classifier is used for classification. The normal and abnormal MRI brain image features are decomposed by DWT, SWT and DMWT. The coefficients of sub-bands are selected by rank features for the classification. Results show that DWT, SWT and DMWT produce 98% accuracy for the MRI brain classification system.

Machine Learning for Brain Images Classification of Two Language Speakers.

The image analysis of the brain with machine learning continues to be a relevant work for the detection of different characteristics of this complex organ. Recent research has observed that there are differences in the structure of the brain, specifically in white matter, when learning and using a second language. This work focuses on knowing the brain from the classification of Magnetic Resonance Images (MRIs) of bilingual and monolingual people who have English as their common language. Different artificial neural networks of a hidden layer were tested until reaching two neurons in that layer. The number of entries used was nine hundred and the classifier registered a high percentage of effectiveness. The training was supervised which could be improved in a future investigation. This task is usually carried out by an expert human with Tract-Based Spatial Statistics analysis and fractional anisotropy expressed in different colors on a screen. So, this proposal presents another option to quantitatively analyse this type of phenomena which allows to contribute to neuroscience by automatically detecting bilingual people of monolinguals by using machine learning from MRIs. This reinforces what is reported in manual detections and the way that a machine can do it.

A CT Scan Brain Image Disease Recognization and Classification Using Isvm Machine Learning Technique

A CT Scan Brain Image Disease Recognization and Classification Using Isvm Machine Learning Technique

Novel scheme of k-SVM analysis using PCA and NN for detection of MRI brain images

The MRI brain image detection and classification is very crucial for medical science. From last few years, various methods are being presented. Here in this paper, we proposed a novel approach to classify a given MRI brain image as normal or abnormal i.e. affected through tumor. The performed method firstly taken the wavelet transform for feature extraction from the images then applied principle component analysis to decrease the dimensions of features. Then the decreased features are being employed to kernel –support vector machine (k-SVM). Here the data has been taken for seven general brain diseases viz. glioma, Alzheimer’s disease, Sarcoma, coma, LINN, IPOL, HPOL. We collected the data of 20 normal and 140 abnormal patients from Harvard Medical Laboratory website. Here four methods of SVM are performed such as L-Kernel, RBF, Poly, and NN. The comparative analysis is also being performed by the existing literatures which illustrates the result by DWT+PCA+KSVM by applying with GRB Kernel. Our classification gives fast and accurate results and assist doctors to distinguish between normal or abnormal to certain precision values.

Deep convolutional neural networks with transfer learning for automated brain image classification

MR brain image categorization has been an active research domain from the last decade. Several techniques have been devised in the past for MR image categorization, starting from classical to the deep learning methods like convolutional neural networks (CNNs). Classical machine learning methods need handcrafted features to perform classification. The CNNs, on the other hand, perform classification by extracting image features directly from raw images via tuning the parameters of the convolutional and pooling layer. The features extracted by CNN strongly depend on the size of the training dataset. If the training dataset is small, CNN tends to overfit after several epochs. So, deep CNNs (DCNNs) with transfer learning have evolved. The prime objective of the present work is to explore the capability of different pre-trained DCNN models with transfer learning for pathological brain image classification. Various pre-trained DCNNs, namely Alexnet, Resnet50, GoogLeNet, VGG-16, Resnet101, VGG-19, Inceptionv3, and InceptionResNetV2, were used in the present study. The last few layers of these models were replaced to accommodate new image categories for our application. These models were extensively evaluated on data from Harvard, clinical, and benchmark Figshare repository. The dataset was then partitioned in the ratio 60:40 for training and testing. The validation on the test set reveals that the pre-trained Alexnet with transfer learning exhibited the best performance in less time compared to other proposed models. The proposed method is more generic as it does not need any handcrafted features and can achieve an accuracy value of 100%, 94%, and 95.92% for three datasets. Other performance measures used in the study include sensitivity, specificity, precision, false positive rate, error, F-score, Mathew correlation coefficient, and area under the curve. The results are compared with both the traditional machine learning methods and those using CNN.

Classification of MRI Brain Images using K-NN and k-means

Classification of MRI Brain Images using K-NN and k-means

Deep Fusion Model Based Enhanced Convolution Neural Networks for MRI Brain Image Classification System

Magnetic Resonance (MR) Imaging is a popular non-invasive modality for the visualization of different abnormalities in the brain due to its good soft-tissue contrast and accessibility of multispectral images. Using information from MR images, CAD systems have been developed to benefit doctors in rapid diagnosis. CAD systems can provide the diagnosis depending upon the specific attributes present in the medical images. The present study proposes a comprehensive method for the diagnosis of the cancerous region in the MRI images. Here, after image noise reduction, optimal image segmentation based on Support Vector Neural neural algorithm is utilized. Afterward, an optimized feature extraction and feature selection based on a modified region growing optimization algorithm are proposed for improving the classification accuracy of brain images. Further, it is also proposed that the input MR brain image be de-noised using a non-local Euclidean median in non-subsampled contourlet space. The classification accuracy of MRG with SVM is 74.24%, MRG with CNN is 82.67% and MRG with ANN is 62.71% and our proposed method MRG with MBCNN is 91.64%.

Medical brain image classification based on multi-feature fusion of convolutional neural network

Medical brain image classification based on multi-feature fusion of convolutional neural network

A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition

Brain image classification is one of the most useful and widely needed processes in the medical system, and it is a highly challenging field. This paper presents a new method for selecting a significant subset of features as the input to the classifier, called mutual information-accelerated singular value decomposition (MI-ASVD). This novel algorithm is exploited to design an intelligent system for classifying MRI brain images into three classes: healthy, high-grade glioma, and low-grade glioma. The proposed system has six stages: pre-processing, clustering, tumour localization, feature extraction, MI-ASVD and classification. First, the MR images are smoothed by using enhancement techniques such as Gaussian kernel filters. Then, local difference in intensity-means (LDI-Means) clustering is employed to segment and detect suspicious regions. The grey-level run-length matrix (GLRLM), texture, and colour intensity features are used for tumour feature extraction. Later, a special method including a summation of feature selection and dimensionality reduction, MI-ASVD, is applied to select the most useful features for the classification process. Finally, the simplified residual neural network technique is implemented to classify the MR brain images. Using MI-ASVD provided accurate and more efficacious results in classification compared with the original feature space and with two other standard dimensionality reduction methods, principal component analysis (PCA) and singular value decomposition (SVD). It achieved a classification accuracy of 94.91%, which is better than the two state-of-the-art techniques as well as methods from similar published studies.

A Hybrid Model of Faster R-CNN and SVM for Tumor Detection and Classification of MRI Brain Images

A Hybrid Model of Faster R-CNN and SVM for Tumor Detection and Classification of MRI Brain Images

A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis.

Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegenerative disorder with progressive memory impairment moreover cognitive functions, while MCI would be considered as a transitional phase amongst age-related cognitive weakening. Numerous machine learning approaches have been examined, aiming at AD computer-aided diagnosis through employing MR image analysis. Conversely, MR brain image changes could be caused by different effects such as aging and dementia. It is still a challenging difficulty to extract the relevant imaging features and classify the subjects of different groups. This paper would propose an automatic classification technique based on feature decomposition and kernel discriminant analysis (KDA) for classifications of progressive MCI (pMCI) vs. normal control (NC), AD vs. NC, and pMCI vs. stable MCI (sMCI). Feature decomposition would be based on dictionary learning, which is used for separation of class-specific components from the non-class-specific components in the features, while KDA would be applied for mapping original nonlinearly separable feature space to the separable features that are linear. The proposed technique would be evaluated by employing T1-weighted MR brain images from 830 subjects comprising 198 AD patients, 167 pMCI, 236 sMCI, and 229 NC from the Alzheimer's disease neuroimaging initiative (ADNI) dataset. Experimental results demonstrate that classification accuracy (ACC) of 90.41%, 84.29%, and 65.94% can be achieved for classification of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, indicating the promising performance of the proposed method.