Automatic Brain Tumor Classification Research Articles

Enhancing Brain Tumor Classification: A CNN-Based Approach with InceptionV3 and Xception

Brain tumors are among the most aggressive and deadly diseases, with a very short life expectancy at the highest grade. To combat this, early detection and treatment are crucial.In this approach, MRI images are used to analyze brain abnormalities. The manual investigation of brain tumor classification is a time-consuming task, and there might be possibilities of human errors. Hence, accurate analysis in a short span of time is essential. This approach presents the automatic brain tumor classification algorithm using a highly accurate Convolutional Neural Network (CNN), InceptionV3 and Xception algorithm for classification of Glioma, Meningioma and No tumor. The brain part is initially segmented by a thresholding approach followed by a morphological operation. The brain MRI is classified using CNN, Inceptionv3, and Xceptionv3 algorithms. The system's performance is evaluated using precision, recall, F1 score, and accuracy parameters.

Brain Tumor Detection Using Deep Learning

The brain tumor is one of the most dangerous, common and aggressive diseases which leads to a very short life expectancy at the highest grade. Thus, to prevent life from such disease, early recognition, and fast treatment is an essential step. In this approach, MRI images are used to analyze brain abnormalities. The manual investigation of brain tumor classification is a time-consuming task and there might have possibilities of human errors. Hence accurate analysis in a tiny span of time is an essential requirement. In this approach, the automatic brain tumor classification algorithm using a highly accurate Convolutional Neural Network (CNN) algorithm is presented. Initially, the brain part is segmented by thresholding approach followed by a morphological operation. The brain MRI is classified using CNN, Inceptionv3, and Xceptionv3 algorithms. The performance of the system is evaluated using precision, recall, F1 score and accuracy parameter

EDLNet: ensemble deep learning network model for automatic brain tumor classification and segmentation

The brain’s abnormal and uncontrollable cell partitioning is a severe cancer disease. The tissues around the brain or the skull induce this tumor to develop spontaneously. For the treatment of a brain tumor, surgical techniques are typically preferred. Deep learning models in the biomedical field have recently attracted a lot of attention for detecting and treating diseases. This article proposes a new Ensemble Deep Learning Network (EDLNet) model. This research uses the Modified Faster RCNN approach to classify brain MRI scan images into cancerous and non-cancerous. A deep recurrent convolutional neural network (DRCNN)-based diagnostic method for early-stage brain tumor segmentation is presented. The evaluation outcomes show that the proposed tumor classification and segmentation model’s performance accurately segments tissues from MRI images. For the analysis of the proposed model, two different publicly available datasets (D1&D2) are used. For D1 and D2 datasets, a total of 99.76% and 99.87% accuracies are achieved by the proposed model. The performance results of the proposed model are more effective than the state-of-the-art network models as per the experimental results. Communicated by Ramaswamy H. Sarma

A Review on Automatic Brain Tumor Classification from MRI Imaging

Abstract: Brain tumor is an irrepressible development of cells that may spread in different tissues. It can be recognized through Magnetic Resonance Imaging (MRI) which is a non-surgical investigation of organ for diagnosing any disease related to the symptoms. Tumors may be cancerous or non-cancerous or it can be considered as life threatening or less dangerous. A tumor belongs to two distinct categories such as benign or malignant. Here benign tumor that has been detected is considered as the non-cancerous or less dangerous and it does not spread to the other part of the brain. It has solid boundaries or contouring that indicates the particular shade of the tumor but malignant is the cancerous tumor which is highly dangerous and it can be spread to the other part of the brain by itself. The boundaries of the malignant tumor are not solid in appearance, instead of that it appears as faded in nature. The intension of the paper is to review different approaches regarding brain tumor classification and finding out certain flaws present in the existing systems.

A REVIEW ON PREDICTIVE BASED BRAIN TUMOR DETECTION TECHNIQUES

The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation (i.e) accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. If tumor is detected system classified the tumor and conveys patient the stage of tumor he is probably suffering.

Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI.

Automatic brain tumor classification is a practicable means of accelerating clinical diagnosis. Recently, deep convolutional neural network (CNN) training with MRI datasets has succeeded in computer-aided diagnostic (CAD) systems. To further improve the classification performance of CNNs, there is still a difficult path forward with regards to subtle discriminative details among brain tumors. We note that the existing methods heavily rely on data-driven convolutional models while overlooking what makes a class different from the others. Our study proposes to guide the network to find exact differences among similar tumor classes. We first present a “dual suppression encoding” block tailored to brain tumor MRIs, which diverges two paths from our network to refine global orderless information and local spatial representations. The aim is to use more valuable clues for correct classes by reducing the impact of negative global features and extending the attention of salient local parts. Then we introduce a “factorized bilinear encoding” layer for feature fusion. The aim is to generate compact and discriminative representations. Finally, the synergy between these two components forms a pipeline that learns in an end-to-end way. Extensive experiments exhibited superior classification performance in qualitative and quantitative evaluation on three datasets.

MRI Image Based Relatable Pixel Extraction with Image Segmentation for Brain Tumor Cell Detection Using Deep Learning Model

Biomedical technology now plays a critical role in the detection and treatment of a wide range of diseases, from minor to life-threatening. One of the most life-threatening disorders is brain tumour, which is defined as a mass development of abnormal cells in the brain. By avoiding the spread of aberrant cells, early discovery and treatment can save a person's life. In the medical field, it is vital to find a certain image categorization strategy based on tumor cell regions. The tumor region is then selected to perform the segmentation process and then classification is performed. The identification-based method helps to limit the image area and to identify the border area in a reduced time period. Automatic brain tumor classification is a difficult undertaking due to the enormous geographical and structural heterogeneity of the brain tumor's surrounding environment. The use of Deep Neural Networks classification for automatic brain tumor detection is proposed. The proposed a Relatable Pixel Extraction with Magnetic Resonance Imaging (MRI) Image Segmentation for Brain Tumor Cell Detection (RPEIS-BTCD) using Deep Learning Model. The proposed model is compared with the existing models and the results indicate that the proposed model performance the accuracy is 97%.

Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis.

Automatic classification of brain tumor plays a vital role to speed up the treatment procedure, plan and boost the survival rate of patients. Nowadays, Magnetic Resonance Imaging (MRI) is employed for determining brain tumor. However, manual identification of brain tumor is purely based on the sensitivity and experience of medical professionals. Thus, more research works towards brain tumor classification have been implemented for minimizing the human factor. Different imaging approaches are employed for detecting brain tumors. Though, MRI is mainly employed owing to the better quality of images due to the non ionizing radiation of images. One of the major categories of machine learning is called deep learning, which shows an outstanding performance, mainly on solving the segmentation and classification issues. The aim of this paper to introduce a new brain tumor classification model based on the intelligent segmentation and classification approaches. The main phases of the proposed model are (a) Data collection, (b) Pre-processing, (c) Tumor segmentation, and (d) Tumor Classification. Initially, the datasets related to the brain tumor are gathered from several benchmark sources and subjected to the pre-processing step. Here, it is performed by the median filtering and contrast enhancement techniques. The first contribution of this paper is the development of an enhanced segmentation approach termed as Adaptive Fuzzy Deformable Fusion (AFDF)-based Segmentation, which merges the two concepts of Fuzzy C-Means Clustering (FCM) and snake deformable approach. Here, the significant parameters of the AFDF are optimized by the improved Deer Hunting Optimization Algorithm (DHOA) termed Adaptive Coefficient Vector-based DHOA (ACV-DHOA). The classification of images is performed by the Optimized Convolutional Neural Network with Ensemble Classification (OCNN-EC) after segmenting the tumor. In the proposed deep learning classification, the number of convolutional layers and hidden neurons of CNN is optimized by the ACV-DHOA, and the fully connected layer is replaced by the ensemble classifier with Deep Neural Network (DNN), autoencoder, and Support Vector Machine (SVM). The classifier which is getting high rank is considered as the optimal one. The experimentation results are performed on the standard database that shows the high classification accuracy of the developed model by evaluating with other conventional methods.

Deep Learning Approach in Brain Tumor Classification

Various Computer-Aided Diagnosis (CAD) systems have been recently used in medical imaging to assist radiologist about their patients. Generally, various image technique such as Computer Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate etc., Especially, in this work MRI images are used to diagnose tumor in the brain. For full assistance of radiologists and better analysis of Magnetic Resonance Imaging, classification of brain tumor is essential procedure. The automatic classification scheme is essential to prevent the death rate of human. Deep learning is the newest and the current trend of machine learning field that paid a lot of the researchers’ attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accuracy and sensitivity, particularly in the medical field. Tumor regions from an MR images are segmented using a deep learning technique. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection using Convolutional Neural Networks (CNN) classification. In general, brain tumor is one of the most common and aggressive malignant tumor diseases which is leading to very short expected life if it is diagnosed at higher grade. The deeper architecture design is performed by using small kernels. Other performance measures used in this study are the accuracy, sensitivity and specificity.

A Reliable and an Efficient Approach for Diagnosis of Brain Tumor Using Transfer Learning

Introduction: The World Brain Tumor Day is seen on eighth June, in a year. Despite exhaustive research in the medical field, the prevalence of this deadly disease is increasing globally with over new 28,000 braintumor cases being reported annually, in India alone. Recent advancements in the field of machine learning facilitate minimally invasive, efficient and reliable procedures for the diagnosis of Brain tumor. Objective: This research intends to design and devlop a reliable framework for accurate diagnosis of brain tumor mainly meningioma type, gliomatype and pituitary cerebrum tumor utilizing Magnetic Resonance Imaging (MRI), one of the most mainstream non-obtrusive procedure Methods: In the proposed system, pre-trained AlexNet is used to classify meningioma, glioma and pituitary brain tumor. The concept of transfer learning is applied using AlexNet for extracting the features from brain MRI images.The AlexNet contains eight layers in which the first five are convolution layer and the remaining three are fully connected layers. The last layer is a softmax layer which gets the output from fully connected layers. The ReLU non-linearity is applied to the output of every convolution and fully connected layer. The idea of transfer learning is applied utilizing AlexNet for computing thefeatures from brain MRI pictures. The AlexNet contains eight layers in which the initial five are convolution layers and the staying three layers are fully connected layers. a softmax is the last layer , which is feeded by the fully connected layers. The ReLUactivation function is applied to the output of convolution layer and fully connectedlayer Result: The proposed system framework recorded the best order precision of 100 % to classify the brain tumor when validated using a practical dataset. Conclusion: The proposed work presents accurate and automatic brain tumor classification using transfer learning. The features extracted using AlexNet has proven to be efficient in obtaining good discriminative power in diagnosis of brain tumor

Classification of Pituitary Tumor and Multiple Sclerosis Brain Lesions through Convolutional Neural Networks

Automatic classification of Brain Tumor and brain Lesions has become a very important step in the field of medical image analytics. The machine learning/Deep learning approaches are playing a tremendous role in the field of medical imaging classification, due to the drastic changes in the field of computing power and image analytics techniques. The deep learning, which is the subfield of machine learning, is playing the major role in the automatic classification of Magnetic Resonance Images (MRIs) having various brain abnormalities. Convolutional Neural Networks are widely used for the classification and detection of various brain disorders. In this research paper, Convolutional Neural Networks are designed with considering various learning parameters for the classification of Multiple Sclerosis Brain Lesions and Pituitary Tumor. In the proposed research, T1-weighted Contrast-enhanced Magnetic Resonance images are preprocessed with various image-preprocessing approaches such as to resize the images, to convert the images into suitable image format so that the experimental work can be performed with deep learning in the Matlab environment. The Experiment is conducted with the dataset of Multiple Sclerosis and Pituitary Tumor each of having 718 and 930T1-weighted MRI images respectively. The experimental results we achieved 99.7% classification accuracy of pituitary Tumor, and 99.2% accuracy of Multiple Sclerosis brain Lesions. The average accuracy of both classifications is 99.55%. The precision of the classification of Pituitary Tumor is 99.7, recall value is 99.7 and the f1_score of the classification is 99.7%. Similarly, the Precision of the classification of Multiple Sclerosis Brain Lesions is 99.15%, the recall value is 99.15%, and the f1_score is 99.15%. The purposed approach of the Convolutional Neural Network architecture exhibited outstanding performance as compared to other research outcomes.

Gaussian hybrid fuzzy clustering and radial basis neural network for automatic brain tumor classification in MRI images

Magnetic Resonance Imaging (MRI) is an emerging research area, employed extensively in radiology for the diagnosis of various neurological diseases. Here, as the classification of MRI gains significant importance for the tumor diagnosis, various techniques are developed in the literature for the automatic classification of MRI images. In this paper, a clustering scheme, namely gaussian hybrid fuzzy clustering (GHFC) is developed by hybridizing the fuzzy c-means (FCM) clustering and sparse FCM along with the Gaussian function for the segmentation purpose. After segmenting the image, the suitable features are extracted from the image and given to the Exponential cuckoo based Radial Basis Neural Network (Exponential cuckoo based RBNN) classifier. The features serve as the training information for the Exponential cuckoo based RBNN classifier, and it finally detects the training class. Simulation of the proposed work is done using BRATS and SIMBRATS databases, and the results are compared with several state-of-art techniques. Simulation results depict that the proposed GHFC along with the RBNN classifier achieved improved accuracy and mean squared error results with the values of 0.8952, and 0.0074, respectively, for the BRATS dataset and 0.8719 and 0.0036, for the SIMBRATS dataset.

Brain MRI Classification using Deep Learning Algorithm

The brain tumor is one of the most dangerous, common and aggressive diseases which leads to a very short life expectancy at the highest grade. Thus, to prevent life from such disease, early recognition, and fast treatment is an essential step. In this approach, MRI images are used to analyze brain abnormalities. The manual investigation of brain tumor classification is a time-consuming task and there might have possibilities of human errors. Hence accurate analysis in a tiny span of time is an essential requirement. In this approach, the automatic brain tumor classification algorithm using a highly accurate Convolutional Neural Network (CNN) algorithm is presented. Initially, the brain part is segmented by thresholding approach followed by a morphological operation. The AlexNet transfer learning network of CNN is used because of the limitation of the brain MRI dataset. The classification layer of Alexnet is replaced by the softmax layer with benign and malignant training images and trained using small weights. The experimental analysis demonstrates that the proposed system achieves the F-measure of 98.44% with low complexity than the state-of-arts method.

Brain Tumor Classification Using Convolutional Neural Networks

The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate…etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation (i.e) accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed by using Convolutional Neural Networks (CNN) classification. The deeper architecture design is performed by using small kernels. The weight of the neuron is given as small. Experimental results show that the CNN archives rate of 97.5% accuracy with low complexity and compared with the all other state of arts methods.

An Efficacious Graphical User Interface Implementation for Automatic Classification of Brain Tumor from Magnetic Resonance Imaging Images Using Image Processing

An Efficacious Graphical User Interface Implementation for Automatic Classification of Brain Tumor from Magnetic Resonance Imaging Images Using Image Processing

A Computer-Based Brain Tumor Detection Approach with Advanced Image Processing and Probabilistic Neural Network Methods

For years, automation in tumor detection area has been commonly required because there might be a shortage of skilled radiologists in this area. In this study, a computer based brain tumor detection approach in magnetic resonance imaging (MRI) is proposed. The purpose of this study is to detect and to localize the tumor areas in the brain with using advanced image processing techniques and probabilistic neural network (PNN) method. For this study, firstly, a special clustering technique is used on the MRI images. Secondly, with using thresholding and level-set segmentation, tumor areas are detected successfully. Thirdly, with using the obtained tumor areas, automatic brain tumor classification and analysis are achieved with using PNN method. In the experimental part of the study, 25 neuroimages are used to optimize our system and 25 out-of-sample neuroimages are also used to test the approach. The preliminary diagnostic results can demonstrate the high classification accuracy for the image processing and the neural network structures.

Computer Assisted Diagnosis of Brain Tumor in MRI Images using Texture Features as Input to Ada-boost Classifier

In medical image processing, segmentation is an important and challenging task. It is classically used to identify object contours and extract the object from the image. Tumor Classification is an significant in medical image analysis since it provides information related to anatomical structures as well as possible anomalous tissues necessary to treatment planning and patient follow-up. In this study, a new approach for automatic classification of brain tumor in enhanced MRI images is developed. Our proposed method consists of Five steps: i) Preprocessing ii) Tumor Region Segmentation iii) Feature Extraction using Wavelet and Level set method iv) Feature Selection and v) Feature Classification using Ada-Boost classifier. The experimental results are validated using the evaluation metrics such as sensitivity, specificity and accuracy. Our proposed system experimental results are compared to other neural network based classifier such as Feed Forward Neural Network (FFNN) and Radial Basics Function (RBF). The classification accuracy of proposed method produces better results compared to other leading tumor classification methods.

Extracting MRS discriminant functional features of brain tumors

The current challenge in automatic brain tumor classification based on MRS is the improvement of the robustness of the classification models that explicitly account for the probable breach of the independent and identically distributed conditions in the MRS data points. To contribute to this purpose, a new algorithm for the extraction of discriminant MRS features of brain tumors based on a functional approach is presented. Functional data analysis based on region segmentation (RSFDA) is based on the functional data analysis formalism using nonuniformly distributed B splines according to spectral regions that are highly correlated. An exhaustive characterization of the method is presented in this work using controlled and real scenarios. The performance of RSFDA was compared with other widely used feature extraction methods. In all simulated conditions, RSFDA was proven to be stable with respect to the number of variables selected and with respect to the classification performance against noise and baseline artifacts. Furthermore, with real multicenter datasets classification, RSFDA and peak integration (PI) obtained better performance than the other feature extraction methods used for comparison. Other advantages of the method proposed are its usefulness in selecting the optimal number of features for classification and its simplified functional representation of the spectra, which contributes to highlight the discriminative regions of the MR spectrum for each classification task.

Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy

JustificationAutomatic brain tumor classification by MRS has been under development for more than a decade. Nonetheless, to our knowledge, there are no published evaluations of predictive models with unseen cases that are subsequently acquired in different centers. The multicenter eTUMOUR project (2004–2009), which builds upon previous expertise from the INTERPRET project (2000–2002) has allowed such an evaluation to take place.Materials and MethodsA total of 253 pairwise classifiers for glioblastoma, meningioma, metastasis, and low-grade glial diagnosis were inferred based on 211 SV short TE INTERPRET MR spectra obtained at 1.5 T (PRESS or STEAM, 20–32 ms) and automatically pre-processed. Afterwards, the classifiers were tested with 97 spectra, which were subsequently compiled during eTUMOUR.ResultsIn our results based on subsequently acquired spectra, accuracies of around 90% were achieved for most of the pairwise discrimination problems. The exception was for the glioblastoma versus metastasis discrimination, which was below 78%. A more clear definition of metastases may be obtained by other approaches, such as MRSI + MRI.ConclusionsThe prediction of the tumor type of in-vivo MRS is possible using classifiers developed from previously acquired data, in different hospitals with different instrumentation under the same acquisition protocols. This methodology may find application for assisting in the diagnosis of new brain tumor cases and for the quality control of multicenter MRS databases.

The effect of combining two echo times in automatic brain tumor classification by MRS

(1)H MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel (1)H MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20-32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis), and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas and oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade glial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may be of use for future web-based multicentric classifier development studies.