Brain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we present an in-depth review of the surveys published so far and recent deep learning-based methods for BTC. Our survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations. We also investigate the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation. Furthermore, this overview describes available benchmark data sets used for the evaluation of BTC. Finally, this survey does not only look into the past literature on the topic but also steps on it to delve into the future of this area and enumerates some research directions that should be followed in the future, especially for personalized and smart healthcare.

Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey

The present work aims to explore the performance of fuzzy system-based medical image processing for brain disease prediction. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. While ensuring the model safety performance, a brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation). Brain MRI images collected from the Department of Brain Oncology, XX Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrated that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 seconds on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under the DSC coefficient, reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. To sum up, the proposed algorithm can provide higher accuracy while ensuring energy consumption, a more apparent denoising effect, and the best segmentation and recognition effect than other models, which can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

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Fuzzy System Based Medical Image Processing for Brain Disease Prediction

The human brain is considered to be the anatomical seat of intelligence, comprehensively supervising conscious and autonomous functions responsible for monitoring and control operations. Although neural homeostasis can be disrupted, early signs of disease should be recognized to save the patient from permanent disability and even a preventable death. The record of World Health Organization (WHO) lists various brain diseases, such as aneurism, stroke and tumor, which affect humans irrespective of their age, sex and province, all of which affect diagnosis, prognosis and treatment options. Since clinically significant diagnosis of brain abnormality is generally performed using dedicated imaging procedures and also under the supervision of an experienced radiologist, more accurate tools can make this process even more precise. The usual protocol involves a radiologist who records the three-dimensional (3D) image which provides initial insight on the type of brain disease, followed by doctor examination of the 3D/2D image that determines the treatment plan. This article proposes a tool and associated procedure to examine a clinical brain image with improved accuracy in order to provide early insight on ideal treatment procedure. In summary, this tool gives the treatment team unprecedented assessment capability before an operation by integrating all the possible image processing procedures to enhance the result in brain image analysis.

A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians

The fuzzy $C$ -means (FCM) clustering procedure is an unsupervised form of grouping the homogenous pixels of an image in the feature space into clusters. A brain magnetic resonance (MR) image is affected by noise and intensity inhomogeneity (IIH) during the acquisition process. FCM has been used in MR brain tissue segmentation. However, it does not consider the neighboring pixels for computing the membership values, thereby misclassifying the noisy pixels. The inaccurate cluster centers obtained in FCM do not address the problem of IIH. A fixed value of the fuzzifier ( ${m}$ ) used in FCM brings uncertainty in controlling the fuzziness of the extracted clusters. To resolve these issues, we suggest a novel type-2 adaptive weighted spatial FCM (AWSFCM) clustering algorithm for MR brain tissue segmentation. The idea of type-2 FCM applied to the problem on hand is new and is reported in this article. The application of the proposed technique to the problem of MR brain tissue segmentation replaces the fixed fuzzifier value with a fuzzy linguistic fuzzifier value ( ${M}$ ). The introduction of the spatial information in the membership function reduces the misclassification of noisy pixels. Furthermore, the incorporation of adaptive weights into the cluster center update function improves the accuracy of the final cluster centers, thereby reducing the effect of IIH. The suggested algorithm is evaluated using T1-w, T2-w, and proton density (PD) brain MR image slices. The performance is justified in terms of qualitative and quantitative measures followed by statistical analysis. The outcomes demonstrate the superiority and robustness of the algorithm in comparison to the state-of-the-art methods. This article is useful for the cybernetics application.

A Novel Type-2 Fuzzy C -Means Clustering for Brain MR Image Segmentation

In recent years, image processing in a Euclidean domain has been well studied. Practical problems in computer vision and geometric modeling involve image data defined in irregular domains, which can be modeled by huge graphs. In this paper, a wavelet frame-based fuzzy $C$ -means (FCM) algorithm for segmenting images on graphs is presented. To enhance its robustness, images on graphs are first filtered by using spatial information. Since a real image usually exhibits sparse approximation under a tight wavelet frame system, feature spaces of images on graphs can be obtained. Combining the original and filtered feature sets, this paper uses the FCM algorithm for segmentation of images on graphs contaminated by noise of different intensities. Finally, some supporting numerical experiments and comparison with other FCM-related algorithms are provided. Experimental results reported for synthetic and real images on graphs demonstrate that the proposed algorithm is effective and efficient, and has a better ability for segmentation of images on graphs than other improved FCM algorithms existing in the literature. The approach can effectively remove noise and retain feature details of images on graphs. It offers a new avenue for segmenting images in irregular domains.

Wavelet Frame-Based Fuzzy C -Means Clustering for Segmenting Images on Graphs

Magnetic resonance imaging (MRI) is extensively applied in clinical practice. Segmentation of the MRI brain image is significant to the detection of brain abnormalities. However, owing to the coexistence of intensity inhomogeneity and noise, dividing the MRI brain image into different clusters precisely has become an arduous task. In this paper, an improved possibilistic fuzzy ${c}$ -means (FCM) method based on a similarity measure is proposed to improve the segmentation performance for MRI brain images. By introducing the new similarity measure, the proposed method is more effective for clustering the data with nonspherical distribution. Besides that, the new similarity measure could alleviate the “cluster-size sensitivity” problem that most FCM-based methods suffer from. Simultaneously, the proposed method could preserve image details as well as suppress image noises via the use of local label information. Experiments conducted on both synthetic and clinical images show that the proposed method is very effective, providing mitigation to the cluster-size sensitivity problem, resistance to noisy images, and applicability to data with more complex distribution.

Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation

In this paper, an intuitionistic center-free fuzzy c-means clustering method (ICFFCM) is proposed for magnetic resonance (MR) brain image segmentation. First, in order to suppress the effect of noise in MR brain images, a pixel-to-pixel similarity with spatial information is defined. Then, for the purpose of handling the vagueness in MR brain images as well as the uncertainty in clustering process, a pixel-to-cluster similarity measure is defined by employing the intuitionistic fuzzy membership function. These two similarities are used to modify the center-free FCM so that the ability of the method for MR brain image segmentation could be improved. Second, on the basis of the improved center-free FCM method, a local information term, which is also intuitionistic and center-free, is appended to the objective function. This generates the final proposed ICFFCM. The consideration of local information further enhances the robustness of ICFFCM to the noise in MR brain images. Experimental results on the simulated and real MR brain image datasets show that ICFFCM is effective and robust. Moreover, ICFFCM could outperform several fuzzy-clustering-based methods and could achieve comparable results to the standard published methods like statistical parametric mapping and FMRIB automated segmentation tool.

Intuitionistic Center-Free FCM Clustering for MR Brain Image Segmentation

Fuzzy c-means (FCM) is a popular clustering method for image segmentation. However, FCM has difficulties in handling artifacts in brain magnetic resonance imaging (MRI), especially when it comes to bias field and noise. We propose a novel multiple-surface-approximation-based FCM with interval membership method for simultaneous bias correction and segmentation of Brain MRI. First, multiple surface representation of bias field is embedded into FCM to estimate and correct bias field. Then memberships of the improved FCM are extended to intervals. After the extension, clustering centers of different MR brain tissues could be solved more properly by the proposed method. Moreover, the proposed method is less sensitive to noise by introducing effects of neighboring pixels. Experiments conducted on artificial images and synthetic and real clinical Brain MRI show that the proposed method is effective and obtains better results of both bias field correction and segmentation than comparing methods.

Multiple-Surface-Approximation-Based FCM With Interval Memberships for Bias Correction and Segmentation of Brain MRI

Current one-stage instance segmentation methods ignore the boundary information of masks, resulting in coarse masks that are far from the ground truth. In this paper, we propose a boundary-aware method to refine boundary information, called BSOLO. The core idea of BSOLO is to design a Hungarian-Algorithm-based boundary loss to calculate matching costs between boundaries. This loss effectively measures the difference between boundaries and suits for boundary regression, contributing to generating refined instance masks with high-quality boundaries. Besides, we propose a Feature Fusion Network (FFN) to capture long-range dependency. Through constructing the relationship between pixels, such a module is beneficial for predicting masks with large or uncontinuous region. Furthermore, we introduce a Prototype Attention Module (PAM) for mask assembling through channel attention, which enhances informative features and spotlights important prototypes. To evaluate the performance of BSOLO, we conduct extensive experiments. Experimental results show that BSOLO achieves 39.3 AP on MS COCO test-dev2017, outperforming SOLO and other methods by a large margin. We hope that BSOLO broadens the perspective for designing more valid boundary constraints.

Yuxuan Zhang

Papers

Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation

Intuitionistic Center-Free FCM Clustering for MR Brain Image Segmentation

Multiple-Surface-Approximation-Based FCM With Interval Memberships for Bias Correction and Segmentation of Brain MRI

BSOLO: Boundary-Aware One-Stage Instance Segmentation SOLO