Performance analysis of computer aided brain tumor detection system using ANFIS classifier

The abrupt changes in brain cells due to the environmental effects or genetic disorders leads to form the abnormal lesions in brain. These abnormal lesions are combined as mass and known as tumor. The detection of these tumor cells in brain image is a complex task due to the similarities between normal cells and tumor cells. In this paper, an automated brain tumor detection and segmentation methodology is proposed. The proposed method consists of feature extraction, classification and segmentation. In this paper, Grey Level Co‐Occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Law's texture features are used as features. These features are fed to Adaptive Neuro Fuzzy Inference System (ANFIS) classifier as input pattern, which classifies the brain image. Morphological operations are now applied on the classified abnormal brain image to segment the tumor regions. The proposed system achieves 95.07% of sensitivity, 99.84% of specificity and 99.80% of accuracy for tumor segmentation.     

A computer‐aided approach for meningioma brain tumor detection using CANFIS classifier

Abnormal growth of cells in brain leads to the formation of tumors in brain. The earlier detection of the tumors in brain will save the life of the patients. Hence, this article proposes a computer‐aided fully automatic methodology for brain tumor detection using Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classifier. The internal region of the brain image is enhanced using image normalization technique and further contourlet transform is applied on the enhanced brain image for the decomposition with different scales. The grey level and heuristic features are extracted from the decomposed coefficients and these features are trained and classified using CANFIS classifier. The performance of the proposed brain tumor detection is analyzed in terms of classification accuracy, sensitivity, specificity, and segmentation accuracy.     

Automated detection of glioblastoma tumor in brain magnetic imaging using ANFIS classifier

This article proposes a novel and efficient methodology for the detection of Glioblastoma tumor in brain MRI images. The proposed method consists of the following stages as preprocessing, Non‐subsampled Contourlet transform (NSCT), feature extraction and Adaptive neuro fuzzy inference system classification. Euclidean direction algorithm is used to remove the impulse noise from the brain image during image acquisition process. NSCT decomposes the denoised brain image into approximation bands and high frequency bands. The features mean, standard deviation and energy are computed for the extracted coefficients and given to the input of the classifier. The classifier classifies the brain MRI image into normal or Glioblastoma tumor image based on the feature set. The proposed system achieves 99.8% sensitivity, 99.7% specificity, and 99.8% accuracy with respect to the ground truth images available in the dataset.     

A novel approach for MR brain tumor classification and detection using optimal CNN-SVM model

The segmentation of brain tumors in magnetic resonance imaging plays a significant role in the field of image processing. This process has high computational complexity when handled manually by clinical experts. The accuracy in classifying and segmenting the brain tumor depends on the radiologists' experience. The computer-aided diagnosis-based brain tumor segmentation approach is proposed to overcome the existing limitations. The proposed convolutional neural network and support vector machine approach consists of the following stages. In the preprocessing stage, unwanted noise and intensity inhomogeneity are suppressed using an anisotropic diffusion filter. Then, the features are extracted using the deep convolutional neural network, and based on the features; the input brain image is classified into normal or abnormal using a support vector machine classifier. The proposed method gives a more successful accuracy rate of 2.11%. Compared with the other methods, the sensitivity and specificity values are also improved to 4.79% and 1.19%.     

Computer aided automated detection and classification of brain tumors using CANFIS classification method

The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.     

Image registration-based brain tumor detection and segmentation using ANFIS classification approach

Abnormal cells in human brain lead to the development of tumors. Manual detection of this tumor region is a time-consuming process. Hence, this paper proposes an efficient and automated computer-aided methodology for brain tumor detection and segmentation using image registration technique and classification approaches. This proposed work consists of the following modules: image registration, contourlet transform, and feature extraction with feature normalization, classification, and segmentation. The extracted features are optimized using genetic algorithm, and then an adaptive neuro-fuzzy inference system classification approach is used to classify the features for the detection and segmentation of tumor regions in brain magnetic resonance imaging. A quantitative analysis is performed to evaluate the proposed methodology for brain tumor detection using sensitivity, specificity, segmentation accuracy, precision, and Dice similarity coefficient.     

An efficient approach for brain image (tissue) compression based on the position of the brain tumor

Medical image processing plays an important role in brain tissue detection and segmentation. In this paper, a computer aided detection of brain tissue compression based on the estimation of the location of the brain tumor. The proposed system detects and segments the brain tissues and brain tumor using mathematical morphological operations. Further, the brain tissue with tumor is compressed using lossless compression technique and the brain tissue without tumor is compressed using lossy compression technique. The proposed method achieves 96.46% sensitivity, 99.20% specificity and 98.73% accuracy for the segmentation of white matter regions from the brain. The proposed method achieves 98.16% sensitivity, 99.36% specificity and 98.78% accuracy for the segmentation of cerebrospinal fluid (CSF) regions from the brain and also achieves 93.07% sensitivity, 98.79% specificity and 97.63% accuracy for the segmentation of grey matter regions from the brain. This paper focus the brain tissue compression based on the location of brain tumor. The grey matter of the brain is applied to lossless compression due to the presence of the tumor in grey matter of the brain. The proposed system achieves 29.23% of compression ratio for compressing the grey matter of the brain region. The white matter and CSF regions of the brain are applied to lossy compression due to the non‐presence of the tumor. The proposed system achieves 39.13% of compression ratio for compressing the white matter and also achieves 37.5% of compression ratio for compressing the CSF tissue. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 237–242, 2016     

Development of computer‐aided approach for brain tumor detection using random forest classifier

The nonlinear development of cells in brain region forms the abnormal patterns in brain in the form of tumors. It is necessary to detect and diagnose the brain tumors in an automated manner using computer‐aided approaches at large population areas. The noises in brain magnetic resonance image is detected and reduced as preprocessing steps and then grey level co‐occurrence matrix are now extracted from the preprocessed brain image. In this article, random forest classifier‐based brain tumor detection and segmentation methodology is proposed to classify the brain image into normal or abnormal. The proposed brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, false‐positive rate, false‐negative rate, likelihood ratio positive, and likelihood ratio negative.     

Glioma tumor detection in brain MRI image using ANFIS‐based normalized graph cut approach

Glioma is the severe type of brain tumor which leads to immediate death for the case high‐grade Glioma. The Glioma tumor patient in case of low grade can extend their life period if tumor is timely detected and providing proper surgery. In this article, a computer‐aided fully automated Glioma brain tumor detection and segmentation system is proposed using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier based Graph cut approach. Initially, orientation analysis is performed on the brain image to obtain the edge enhanced abnormal regions in the brain. Then, features are extracted from the orientation enhanced image and these features are trained and classified using ANFIS classifier to classify the test brain image into either normal or abnormal. Normalized Graph cur segmentation methodology is applied on the classified abnormal brain image to segment the tumor region. The proposed Glioma tumor segmentation method is validated using the metric parameters as sensitivity, specificity, accuracy and dice similarity coefficient.     

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain-related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas-Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary results on different orientations, sequences of both of these datasets as per accuracy (up to 99.6%), sensitivity (up to 100%), specificity (up to 100%), precision (up to 100%), and AUC value (up to 1.0) have been achieved. The tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.     

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non-Glioma image based on the optimized set of features. Furthermore, energy-based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy.     

Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm

In the last decade, there has been a significant increase in medical cases involving brain tumors. Brain tumor is the tenth most common type of tumor, affecting millions of people. However, if it is detected early, the cure rate can increase. Computer vision researchers are working to develop sophisticated techniques for detecting and classifying brain tumors. MRI scans are primarily used for tumor analysis. We proposed an automated system for brain tumor detection and classification using a saliency map and deep learning feature optimization in this paper. The proposed framework was implemented in stages. In the initial phase of the proposed framework, a fusion-based contrast enhancement technique is proposed. In the following phase, a tumor segmentation technique based on saliency maps is proposed, which is then mapped on original images based on active contour. Following that, a pre-trained CNN model named EfficientNetB0 is fine-tuned and trained in two ways: on enhanced images and on tumor localization images. Deep transfer learning is used to train both models, and features are extracted from the average pooling layer. The deep learning features are then fused using an improved fusion approach known as Entropy Serial Fusion. The best features are chosen in the final step using an improved dragonfly optimization algorithm. Finally, the best features are classified using an extreme learning machine (ELM). The experimental process is conducted on three publically available datasets and achieved an improved accuracy of 95.14, 94.89, and 95.94%, respectively. The comparison with several neural nets shows the improvement of proposed framework.     

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

We have developed six convolutional neural network (CNN) models for finding optimal brain tumor detection system on high-grade glioma and low-grade glioma lesions from voluminous magnetic resonance imaging human brain scans. Glioma is the most common form of brain tumor. The models are constructed based on the different combinations and settings of hyperparameters with conventional CNN architecture. The six models are two layers with five epochs, five layers with dropout, five layers with stopping criteria (FLSC), FLSC and dropout (FLSCD), FLSC and batch normalization (FLSCBN), and FLSCBN and dropout. The models were trained and tested with BraTS2013 and whole brain atlas data sets. Among them, FLSCBN model yielded the best classification results for brain tumor detection. Experimental results revealed that our deep learning approach was better than the conventional state-of-art methods.     

Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier

Abnormal growth of cells in brain leads to the formation of tumors, which are categorized into benign and malignant. In this article, Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification based brain tumor detection and its grading system is proposed. It has two phases as brain tumor segmentation and brain tissue segmentation. In brain tumor segmentation, CANFIS classifier is used to classify the test brain image into benign or malignant. Then, morphological operations are applied over the malignant image in order to segment the tumor regions in brain image. The K‐means classifier is used to classify the brain tissues into Grey Matter (GM), White Matter (WM) and Cerebro Spinal Fluid (CSF) regions as three different classes. Next, the segmented tumor is graded as mild, moderate or severe based on the presence of segmented tumor region in brain tissues.     

Lung computed axial tomography image segmentation using possibilistic fuzzy C-means approach for computer aided diagnosis system

Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C-means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C-means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results.     

Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

A fast and efficient computer aided diagnostic system to detect tumor from brain magnetic resonance imaging

In this work, a simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI). Poor contrast MR images are preprocessed by using morphological operations and DSR (dynamic stochastic resonance) technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest (ROI) lies in the middle and its size ranges from 240 × 240 mm² to 280 × 280 mm². The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block‐based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐means, fuzzy c‐means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work.     

Automatic liver tumor segmentation on multiphase computed tomography volume using SegNet deep neural network and K-means clustering

Liver and liver tumor segmentations are essential in computer-aided systems for diagnosing liver tumors. These systems must operate on multiphase computed tomography (CT) images instead of a single phase for accurate diagnosis for clinical applications. We have proposed a framework that can perform segmentation from quadriphasic CT data. The liver was segmented using a fine-tuned SegNet model and the liver tumor was segmented using the K-means clustering method coupled with a power-law transformation-based image enhancement technique. The best values for liver segmentation achieved were: Dice Coefficient = 96.46 ± 0.48%, Jaccard Index = 93.16 ± 0.89%, volumetric overlap error = 6.84 ± 0.89% and average symmetric surface distance = 0.59 ± 0.3 mm and the results for liver tumor delineation were Dice Coefficient = 85.07 ± 4.5%, Jaccard Index = 74.29 ± 6.8%, volumetric overlap error = 25.71 ± 6.8% and average symmetric surface distance = 1.14 ± 1.3 mm. The proposed liver segmentation method based on deep learning is fully automatic, robust, and effective for all phases. The image enhancement technique has shown promising results and aided in better liver tumor segmentation. The liver tumors were segmented satisfactorily; however, improvements concerning false positive reduction can further increase the accuracy.     

Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images

A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a texture analysis methods to find and select the texture features of the tumor region of each slice to be segmented by support vector machine (SVM). The images considered for this study belongs to 208 benign and malignant tumor slices. The features are extracted and selected using Student's t‐test. The reduced optimal features are used to model and train the probabilistic neural network (PNN) classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of quantitative measure of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features have important contribution in segmenting and classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed hybrid texture feature analysis method using Probabilistic Neural Network (PNN) based classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by Jaccard index, sensitivity, and specificity.