Improved Fuzzy C-means Algorithm Research Articles

Fuzzy C-means clustering algorithm applied in computed tomography images of patients with intracranial hemorrhage.

In recent years, intracerebral hemorrhage (ICH) has garnered significant attention as a severe cerebrovascular disorder. To enhance the accuracy of ICH detection and segmentation, this study proposed an improved fuzzy C-means (FCM) algorithm and performed a comparative analysis with both traditional FCM and advanced convolutional neural network (CNN) algorithms. Experiments conducted on the publicly available CT-ICH dataset evaluated the performance of these three algorithms in predicting ICH volume. The results demonstrated that the improved FCM algorithm offered notable improvements in computational time and resource consumption compared to the traditional FCM algorithm, while also showing enhanced accuracy. However, it still lagged behind the CNN algorithm in areas such as feature extraction, model generalization, and the ability to handle complex image structures. The study concluded with a discussion of potential directions for further optimizing the FCM algorithm, aiming to bridge the performance gap with CNN algorithms and provide a reference for future research in medical image processing.

EM-IFCM: Fuzzy c-means clustering algorithm based on edge modification for imbalanced data

The improved fuzzy c-means (IFCM) algorithm is an effective technique for handling the “uniform effect” in imbalanced data clustering; it adjusts the weight of each class based on the fuzzy size between clusters. However, the IFCM algorithm produces a “siphon effect” as the imbalance rate increases. It misclassifies the samples in small classes into large ones. Our analysis shows that this effect occurs because all samples have the same weight value of the same classes, the membership values are polarized, resulting in the model failing to converge to the correct interval. Thus, we propose an imbalanced fuzzy c-means clustering based on edge modification (EM-IFCM) algorithm to alleviate the “siphon effect” of the IFCM algorithm. It exhibits stronger inter-class separability by dynamically adjusting the weight of the samples to enhance the influence of edge samples on the model. In addition, we analyze the effectiveness and complexity of the algorithm and proved its convergence. Finally, we conduct extensive experiments on synthesis, machine-learning, and image-segmentation datasets and compare the results with those of six algorithms. The experimental results show that EM-IFCM has higher accuracy and exhibits an imbalance rate that is at least 1.94 times higher than that of the other algorithms.

Module division method of complex products for responding to user’s requirements

With the gradual diversification of personalized usage scenarios, user requirements play a direct role in product design decisions. Due to the problem of fuzzy demand caused by user cognitive bias, traditional design methods usually focus on realizing product functions and cannot effectively match user requirements. Therefore, this paper proposes a complex product module division method for user requirements. The method constitutes of three tasks, requirement analysis of module division, design mapping of module division and scheme implementation of module division. Firstly, based on the progressive architecture from initial requirements to precise requirements, the effective user requirements are obtained through similarity recommendation. Secondly, according to the four types of knowledge of function, geometry, physics and design, the design structure matrix is constructed to complete the Requirement-Function-Structure mapping. The improved Fuzzy C-means Algorithm is used to solve the mapping model, and finally a module division scheme that meets the user requirements is obtained. Taking the chip removal machine as an example, the rationality and effectiveness of the method are verified. The results show that the product modules divided by this method can effectively meet the multiple user requirements.

Detection and location of microaneurysms in fundus images based on improved YOLOv4 with IFCM

AbstractDiabetic retinopathy (DR) is one of the leading causes of blindness for people suffering from diabetes. Microaneurysm (MA) is the initial symptom of DR. MA is a lesion based disease which starts as small red spots on the retina and increases in size as the DR progresses which finally leads to blindness. So eliminating the lesion can effectively prevent DR at an early stage. However, due to complex retinal structure, different brightness and contrast of fundus images with effects of factors such as different patients, environment changes, and difference in acquisition equipment, it is difficult for existing detection algorithms to achieve accurate results of MA detection and location. Therefore, the detection algorithm of improved YOLOv4 (YOLOv4‐Pro) was proposed. First, an improved Fuzzy C‐Means (IFCM) clustering algorithm was proposed to optimize anchor parameters of target samples to improve matching results between anchors and feature graphs. In order to control noise and improve efficiency, a median filtering method was employed to update the criterion function of the original FCM algorithm, and a K‐means algorithm was employed to initialize clustering. Second, a SENet attention module was added in the backbone of YOLOv4 to enhance key information and suppress background, improving the confidence of MA effectively. Finally, the spatial pyramid pooling (SPP) module was added to the neck to enhance the acceptance domain of the output characteristics of the backbone network, and profits separating of important context information. The improved YOLOv4 with IFCM was verified on the Kaggle DR dataset and compared with other methods. Experimental results show that optimizing the prior frame with the IFCM algorithm can make it suitable to frame the Kaggle DR dataset, which improves the detection accuracy of the network by nearly 5%, and provides a nice performance on detection and location of MA in fundus images. This would help ophthalmologists finding the exact location of MA on retina, thereby simplifying the process and eliminating any manual intervention.

Temperature-Sensitive Points Optimization of Spindle on Vertical Machining Center with Improved Fuzzy C-Means Clustering

The heat generated by motors and bearings of machine tools has a significant impact on machining accuracy. Error modeling and compensation has proven to be effective ways to reduce thermal errors and improve accuracy. An improved fuzzy c-means (FCM) clustering algorithm is proposed to determine the optimized temperature sensitive points for thermal error modeling of a spindle on the vertical machining center. The sensors are deployed to measure the temperature of different positions of machine tools, and the improved FCM algorithm is used to classify the measured data. Combined with the F-test statistics of multiple linear regression, the optimal temperature points of each group are selected. The improved FCM clustering algorithm significantly reduces the multicollinearity problem among temperature measuring points and avoids them falling into local optimization. The modeling method was verified through experiments on two types of vertical machining centers. The results show that the accuracy of the spindle in Y and Z directions of the machine tools was increased by more than 75%, and the model has good robustness, demonstrating application prospects in the selection of temperature measuring points of the spindle system of vertical machining centers.

RETRACTED ARTICLE: Comparative analysis of improved FCM algorithms for the segmentation of retinal blood vessels

The main purpose of identifying and locating the retina vessels is to specify from the fundus image the various tissues of the vascular structure, which can be wide or tight. The classification of vessels in the retinal image often confronts several challenges, such as the low contrast accompanying the fundus image, the inhomogeneity of the background lighting, and the noise. Moreover, fuzzy c-means (FCM) is one of the most frequently used algorithms for medical image segmentation due to its effectiveness. Hence, many FCM method derivatives have been developed to improve their noise robustness and time-consuming. This paper aims to analyze the performance of some improved FCM algorithms to recommend the best ones for the segmentation of retinal blood vessels. Eight derivatives of FCM algorithm are detained in this study: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and SSFCA. The performance analysis is conducted from three viewpoints: noise robustness, blood vessels segmentation performance, and time-consuming. At first, the noise robustness of improved FCM clustering algorithms is evaluated using a synthetic image degraded by various types and levels of noise. Then, the ability of the selected algorithms to segment retinal blood vessels is assessed based on images from the DRIVE and STARE databases after a pre-processing phase. Finally, the time consumption of each algorithm is measured. The experiments demonstrate that the FRFCM and DSFCM_N algorithms achieve better results in terms of noise robustness and blood vessels segmentation. Regarding the running time, the FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images. The results of this study are extensively discussed, and some suggestions are proposed at the end of this paper.

A Repair Method for Missing Traffic Data Based on FCM, Optimized by the Twice Grid Optimization and Sparrow Search Algorithms.

Complete traffic sensor data is a significant prerequisite for analyzing the changing rules of traffic flow and formulating traffic control strategies. Nevertheless, the missing traffic data are common in practice. In this study, an improved Fuzzy C-Means algorithm is proposed to repair missing traffic data, and three different repair modes are established according to the correlation of time, space, and attribute value of traffic flow. First, a Twice Grid Optimization (TGO) algorithm is proposed to provide a reliable initial clustering center for the FCM algorithm. Then the Sparrow Search Algorithm (SSA) is used to optimize the fuzzy weighting index m and classification number k of the FCM algorithm. Finally, an experimental test of the traffic sensor data in Shunyi District, Beijing, is employed to verify the effectiveness of the TGO-SSA-FCM. Experimental results showed that the improved algorithm had a better performance than some traditional algorithms, and different data repair modes should be selected under different miss rate conditions.

Cognitive Impairment of Patient With Neurological Cerebrovascular Disease Using the Artificial Intelligence Technology Guided by MRI.

This study was to explore the application of MRI based on artificial intelligence technology combined with neuropsychological assessment to the cognitive impairment of patients with neurological cerebrovascular diseases. A total of 176 patients were divided into a control group, a vascular cognitive impairment non-dementia (VCIND) group, a vascular dementia (VD) group, and an Alzheimer's disease (AD) group. All patients underwent MRI and neuropsychological evaluation and examination, and an improved fuzzy C-means (FCM) clustering algorithm was proposed for MRI processing. It was found that the segmentation accuracy (SA) and similarity (KI) data of the improved FCM algorithm used in this study were higher than those of the standard FCM algorithm, bias-corrected FCM (BCFCM) algorithm, and rough FCM (RFCM) algorithm (p < 0.05). In the activities of daily living (ADL), the values in the VCIND group (23.55 ± 6.12) and the VD group (28.56 ± 3.1) were higher than that in the control group (19.17 ± 3.67), so the hippocampal volume was negatively correlated with the ADL (r = −0.872, p < 0.01). In the VCIND group (52.4%), VD group (31%), and AD group (26.1%), the proportion of patients with the lacunar infarction distributed on both sides of the brain and the number of multiple cerebral infarction lesions (76.2, 71.4, and 71.7%, respectively) were significantly higher than those in the control group (23.9 and 50%). In short, the improved FCM algorithm showed a higher segmentation effect and SA for MRI of neurological cerebrovascular disease. In addition, the distribution, number, white matter lesions, and hippocampal volume of lacunar cerebral infarction were related to the cognitive impairment of patients with cerebrovascular diseases.

Value of Magnetic Resonance Images and Magnetic Resonance Spectroscopy in Diagnosis of Brain Tumors under Fuzzy C-Means Algorithm.

This study was aimed to explore the diagnostic value of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in brain tumors under the fuzzy C-means (FCM) algorithm. The two-dimensional FCM hybrid algorithm was improved to be three-dimensional. The MRI images and MRS spectra of 127 patients with brain tumors (low-grade glioma group) and 54 healthy people (healthy group) were analyzed. The results suggested that the membership matrix of the improved algorithm had lower ambiguity, higher segmentation accuracy, closer relationship of intrapixels, and stronger irrelevance of interclass pixels. Through the analysis of gray matter volume, it was found that, compared with the healthy group, the gray matter and white matter volumes in the brain of high-grade glioma were higher, and those of low-grade glioma group were lower. The improved FCM algorithm could obtain a higher accuracy of 88.64% in segmenting images. It had a higher sensitivity to gray matter changes in brain tumors, reaching 92.72%; its specificity was not much different from that of traditional FCM, which were 83.61% and 88.06%, respectively. In the diagnostic value, the area under the curve of mean kurtosis was the largest, which was 0.962 (P < 0.001). The best critical value was 0.4096, which had a greater reference significance for clinical treatment and prognosis. The ratio of choline/N-acetyl-aspartate and the ratio of choline/creatine also showed significant differences in high- and low-grade gliomas (P < 0.05), but the specificity and sensitivity were slightly lower. It also had guiding significance for the grading of gliomas. Overall, the improved FCM algorithm had obvious advantages in the segmentation process of MRI images, which provided help for the clinical diagnosis of brain tumors.

Evaluation of Functional Magnetic Resonance Imaging under Artificial Intelligence Algorithm on Plan-Do-Check-Action Home Nursing for Patients with Diabetic Nephropathy.

This study aimed to evaluate the effect of functional magnetic resonance imaging (fMRI) under the fuzzy C-means (FCM) clustering algorithm on plan-do-check-action (PDCA) home nursing for patients with diabetic nephropathy (DN). As the characteristics of fMRI image data were combined, the FCM algorithm was improved and applied into the clustering processing of fMRI activation regions of patients. 64 patients with DN were chosen as the research objects and were divided into the research group with PDCA home nursing and the control group with routine home nursing. The patients were randomly divided into the research group (n = 32) and the control group (n = 32). The curative effect, nursing satisfaction, and quality of life of patients after nursing were compared. The results showed that the coverage of fMRI activation points was significantly higher as being detected by the FCM algorithm, and the running time was shortened by 33.6 min. After nursing, the total effective rates in the research group and the control group were 87.5% vs. 34.4% in 3 months, 93.8% vs. 68.8% in 6 months, and 96.9% vs. 75.0% in 12 months, respectively; those in the research group were significantly higher than those in the control group (P < 0.05). The nursing satisfaction score (91.3 ± 4.5 vs. 80.9 ± 5.2) and nursing service quality score (89.7 ± 6.6 vs. 80.3 ± 7.1) in the research group were also significantly higher than those in the control group (P < 0.05). Meanwhile, the scores of each item after nursing in the research group were significantly higher than those in the control group (P < 0.05). The improved FCM algorithm detected the activation regions in the fMRI images more effectively, which could provide help for diagnosis and reduce error and misdiagnosis. At the same time, the PDCA home nursing also offered great help to the recovery of patients with DN, which was more superior for the curative effect of hospitalization, the promotion of recovery, and the improvement of patients' quality of life.

Improved fuzzy C-means clustering algorithm based on t-SNE for terahertz spectral recognition

Terahertz (THz) waves characterized by low energy, instantaneity and proficiency in spectral analysis, have promising future in material identification. The main dimensionality reduction methods of Terahertz-based material identification, including Principal Component Analysis (PCA), Local Preserving Projection (LPP) and Local Linear Embedding (LLE), are sensitive to the number of nearest neighbor samples and neglect difference among classes, thus making it difficult to design the subsequent clustering model or leading to incorrect clustering. The t-distributed Stochastic Neighbor Embedding (t-SNE), which regards the sample distribution in the high-dimension as a Gaussian distribution and the coordinates in the low-dimension as t-distribution, makes the distance of clusters with long distance longer and then relieve their congestion. Besides, in the traditional Fuzzy C-means (FCM) clustering methods, the initial clustering center is randomly determined, so the problem of local optimum is easy to appear, which is prone to cause wrong recognition. To solve the above problems, an improved FCM algorithm is proposed in this paper for Terahertz spectral recognition. Firstly, t-SNE method is used for dimensional reduction and for the selection of initial clustering center for a more accurate clustering effect. On this basis, classical FCM clustering is used to recognize different substances through Terahertz spectrum. The algorithm can not only relieve the congestion among classes in the process of clustering, but also reflect the distance there for an appropriate cluster center in samples. In order to verify the reliability of the proposed method, the Terahertz time-domain spectroscopy is used to detect three genetically modified cotton seeds of lumianyan28, lumianyan29 and lumianyan36 respectively, obtaining their time-domain spectral data. It is the proposed method, which is used to analyze the spectral data, that successfully distinguishes three different types of transgenic cotton seeds, with a total accuracy of 0.9668. The result shows that the clustering method proposed in the paper has a bright prospect in identifying the Terahertz spectrum of materials.

Semi-supervised Fault Diagnosis Model Based on Improved Fuzzy C-means Clustering and Convolutional Neural Network

The number of unlabeled data is much more than labeled data. There are some challenges in using existing complex data for fault diagnosis. Therefore, we proposed a semi-supervised fault diagnosis algorithm based on improved fuzzy C-means algorithm (IFCM) and Convolutional Neural Network (CNN). Firstly, the fault original signal is extracted by variational mode decomposition (VMD) and the singular value decomposition (SVD) to extract the fault feature vector. Secondly, IFCM is used to obtain the membership degree of the data to all categories, which integrates the labeled data into the cluster center of fuzzy C-means (FCM) to obtain better clustering results. The label of labeled data will spread to unlabeled data in the same category. Thirdly, data with a membership degree greater than the value of 0.9 will be selected as the core data set. Finally, the original signal and labels of core data set will be input into CNN for fault diagnosis. The result shows that the fault recognition rate of IFCM-CNN reaches 95%. The fault diagnosis effect is significantly better than other algorithms.

A Quick Screening Approach Based on Fuzzy C-Means Algorithm for the Second Usage of Retired Lithium-Ion Batteries

With numerous lithium-ion batteries retired from electric vehicles, the studies on the battery second usage are extremely imminent. However, existing screening approaches on plenty of cells fail to guarantee high efficiency and high accuracy simultaneously. This article proposes a quick and accurate screening method based on the improved fuzzy c-means (FCM) algorithm. First, the partial charging curve of every single cell is selected optimally based on the incremental capacity analysis, which is frequently used to detect the battery aging mechanism. Second, four important features are extracted from the partial charging curves, including key point, curve gradient, voltage energy, and volatility. Furthermore, feature optimization is done by observing the relationship between capacity and feature. Finally, the retired batteries are screened with the optimal features using the improved FCM algorithm. The screening result on 176 LiFePO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> batteries proves the high accuracy and high efficiency of the approach. Compared with the support vector machine and neural network approaches, the proposed method has better generality and higher efficiency without pretreatment training. The screening accuracy can reach 90.9%. With a permitted error of 1%, it can be as high as 95.5%. The screening efficiency is about 7.6 times higher than the supervised screening method.

Automatic cloud segmentation from INSAT‐3D satellite image via IKM and IFCM clustering

Cloud extraction and classification from satellite imagery is important for many applications in remote sensing. Satellite images are segmented based on distance, intensity and texture of the images. The popular segmentation algorithms, k-means (KM) and fuzzy c-means (FCM) clustering algorithms, face some problems such as unknown number of groups, unknown initialization and dead centers. In this paper, an unsupervised pixel classification by the KM and FCM algorithms is improved and the selection of centroids is made automatic. The proposed improved k-means (IKM) and improved fuzzy c-means (IFCM) clustering algorithms segment the INSAT-3D satellite's thermal infrared image into low-level, middle-level, high-level clouds and non-cloudy region. As human beings can easily find the clouds in the satellite images, visible image is used to differentiate the clouds from the background. A threshold is found from the histogram of the visible image to separate the cloudy and non-cloudy pixels. The other three thresholds to divide the clouds into three types are found from the thermal infrared image's histogram. The segmentation results of IKM and IFCM algorithms are compared with the existing segmentation algorithms. The comparison shows that IFCM algorithm matches well with original image followed by IKM algorithm as compared with existing algorithms.

Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning

Image segmentation is an active research topic in image processing. The Fuzzy C-means (FCM) clustering analysis has been widely used in image segmentation. As there is a large amount of delicate tissues such as blood vessels and nerves in medical images, noise generated during imaging process can easily affect successful segmentation of these tissues. The traditional FCM algorithm is not ideal for segmentation of images containing strong noise. In this study, we proposed an improved FCM algorithm with anti-noise capability. We first discussed the algorithm of dictionary learning for noise reduction. Then we developed a new image segmentation algorithm as a combination of the dictionary learning for noise reduction and the improved fuzzy C-means clustering. Lastly we used the algorithm of the improved FCM to segment images, during which we removed the non-target areas making use of the grayscale features of images and extracted accurately the areas of interests. The algorithm was tested using synthetic Shepp-Logan images and real medical magnetic resonance imaging (MRI) and computed tomography (CT) images. Compared to the synthetic data and real medical images segmented by the fuzzy C-means (FCM) clustering algorithm, the Kernel Fuzzy C-mean (KFCM) clustering algorithm, spectral clustering algorithm, the sparse learning based fuzzy C-means (SL_FCM) clustering algorithm, and the modified spatial KFCM (MSFCM) algorithm, the images segmented by the dictionary learning Fuzzy C-mean clustering (DLFCM) algorithm have higher partition coefficient, lower partition entropy, better visual perception, better clustering accuracy, and clustering purity.

Residual-Sparse Fuzzy C-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frame

Instead of directly utilizing an observed image including some outliers, noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free image) has a favorable impact on clustering. Hence, the accurate estimation of the residual (e.g. unknown noise) between the observed image and its ideal value is an important task. To do so, we propose an $\ell_0$ regularization-based Fuzzy $C$-Means (FCM) algorithm incorporating a morphological reconstruction operation and a tight wavelet frame transform. To achieve a sound trade-off between detail preservation and noise suppression, morphological reconstruction is used to filter an observed image. By combining the observed and filtered images, a weighted sum image is generated. Since a tight wavelet frame system has sparse representations of an image, it is employed to decompose the weighted sum image, thus forming its corresponding feature set. Taking it as data for clustering, we present an improved FCM algorithm by imposing an $\ell_0$ regularization term on the residual between the feature set and its ideal value, which implies that the favorable estimation of the residual is obtained and the ideal value participates in clustering. Spatial information is also introduced into clustering since it is naturally encountered in image segmentation. Furthermore, it makes the estimation of the residual more reliable. To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, the segmented image is reconstructed by using a tight wavelet frame reconstruction operation. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.

Research on Urban Resident Activity Patterns and Hotspot Area Based on GPS Floating Car Data

With the rapid development of urbanization, the living standard has been improved on a continued basis for urban residents and their activities have become increasingly frequent. Therefore, it is of massive significance to study the hot spots of urban residents' activities and enforce effective planning and decision-making for urban and traffic departments. In this paper, the data is preprocessed in the first place. Then, the passengers' pick-up points and travel track points are extracted, and the statistical analysis method is employed to analyze the travel length and travel time of urban residents. Finally, an improved FCM algorithm is proposed. The conventional Fuzzy c-means (FCM) clustering algorithm is classed as a local optimal algorithm, and the number of clustering is made uncertain. In view of the shortcomings as mentioned above, an improved (FCM) clustering algorithm is suggested in this paper, which adopts adaptive distance norm and adds its own norm induction matrix to each cluster in order to ensure global optimization. The partition coefficient (PC), classification entropy (CE) and (XB) index are introduced to assist in determining the optimal number of clustering. According to the statistical analysis of GPS track data, the morning peak and evening peak in a day is 7:00-10:00 and 17:00-20:00, respectively. Cluster analysis is carried out for each time period and the whole day using the model proposed in this paper. The results show that the number of hot spots in each time period and the whole day is 12, 10, 8, 13, 13, respectively. The hot spots are distributed in the business center, office and residential areas, which are consistent with the actual situation. It shows that the model proposed in this paper can effectively and accurately mine the hot spots of urban residents' activities. It plays an important role in urban planning and commercial layout.

An Automated Map Process Based Improved Fuzzy C-Means Algorithm for Pathological Detection in MR Image

Automated brain MR slices segmentation process is difficult, and further difficult is the process of detecting the tumor and tissue regions, with a constraint of delivering higher segmentation accuracy within reduced processing time. Automated algorithms were developed with an onus of reducing the intricacies involved during the manual inspection of the pathologies (radiologist/operator involvement). The shortages of an automated process are overthrown with the development of a novel combination of soft computing algorithms, and it employs automated map and clustering approaches. Self-Organizing map (SOM) and Improved Fuzzy C-Means clustering (IFCM) are the automated map and clustering approaches that are used to precisely provide the MRI slice analysis. The authors have utilized the quality metrics, such as Dice overlap Index (DOI), Jaccard index, Peak Signal to Nosie Ratio (PSNR) and Mean Squared Error (MSE) for verifying the performance of the SOM based IFCM, and the recommended algorithm tenders the corresponding values of the above as 84.83%, 91.69%, 0.0824 and 49.25dB. The novel SOM- IFCM algorithm delivers better demarcation outcomes when compared with other soft computing approaches. The exemplified outcomes of the proposed SOMIFCM algorithm provides superior segmentation quality of MR brain slices and offers versatile usage to the radiologists

Improved Methods for Brain Tumor Detection and Analysis Using MR Brain Images

Medical image processing techniques play an important role in helping doctors and facilities for patient diagnosis, the aim of this paper is comparison between three improved methods to identify the brain tumor using magnetic resonance brain images and analysis of the performance of each method according to different values, accuracy, nJaccard coeff, ndice, sensitivity, specificity, recall and precision values,We used three improved methods the first method improved fuzzy c-means algorithm (IFCM), the second method is improved feed-forward neural network (IFFNN), and the third method is a hybrid self-organizing map with a fuzzy k-means algorithm,the significance of these methods is complementary among them where each one has an advantage in a certain value as shown in the paper results, the three methods gave a very good performance, generally they can identify the tumor area clearly in MR brain image with different performance of the values, each method gave better values than others according to a comparison between the performance value of three methods,Finally, the improved methods allow the development of algorithms to diagnose a tumor more accurately and for a short period of time and each method is distinguished from each other in the performance and value, this gives integrity and strength to this work, these methods can be used in pre and post radio surgical applications

Improved fuzzy C-means algorithm based on density peak

Fuzzy C-means (FCM) algorithm is a fuzzy clustering algorithm based on objective function compared with typical “hard clustering” such as k-means algorithm. FCM algorithm calculates the membership degree of each sample to all classes and obtain more reliable and accurate classification results. However, in the process of clustering, FCM algorithm needs to determine the number of clusters manually, and is sensitive to the initial clustering center. It is easy to generate problems such as multiple clustering iterations, slow convergence speed and local optimal solution. To address those problems, we propose to combine the FCM algorithm and DPC (Clustering by fast search and find of density peaks) algorithm. First, DPC algorithm is used to automatically select the center and number of clusters, and then FCM algorithm is used to realize clustering. The comparison experiments show that the improved FCM algorithm has a faster convergence speed and higher accuracy.