A novel optimization of hybrid feature selection algorithms for image classification technique using RBFNN and MFO

A brain tumor develops when abnormal cells in brain tissue multiply uncontrollably. For radiologists, finding and categorizing tumors manually has become a demanding and time-consuming task. When radiologists or other clinical professionals need to extract an infected tumor area from an MR picture, they have to go through a lengthy and laborious process. To improve performance and simplify the segmentation process, we investigate the FCM-predicted picture segmentation techniques in this study. In addition, classifiers for automating the detection and reclassification of encephalon tumors receive input consisting of critical information obtained from each segmented tissue. We have assessed, verified, and demonstrated the experimental efficacy of the proposed method. The purpose of this research was to develop a novel MFO (Moth-Flame Optimization) based LLRBFNN model for the automatic detection and classification of benign and malignant brain tumors. In order to alleviate the burden of manually detecting encephalon cancers from MR images, the suggested LLRBFNN model parameters are improved via MFO training. The Modified FCM method removes outlying nodes from the LLRBFNN model, and the MFO algorithm keeps the current of node centres in the aforementioned model. The proposed MFO-LLRBFNN model was evaluated alongside the Decision Tree and the Random Forest. To prove the reliability of this model, an MFO-based LLWNN (Local Linear Wavelet Neural Network) model for autonomously detecting brain cancers was presented. We extracted features from MR images using the MFCM (modified fuzzy C-Means) segmentation algorithm and the GLCM (Gray Level Co-occurrence Matrix) technique.


INTRODUCTION
Dimensionality reduction is required before extracting the most informative features from a medical image. (3)Using this feature extraction technique will be quite helpful when working with large picture sizes, and a reduced feature demonstration may be necessary to swiftly finish tasks like MRI image matching and retrieval. (1)sing novel segmentation approaches and classifiers, (4,5) this study aims to detect and categorize brain cancers.Inaccurate tumor detection has been a problem with current segmentation methods such as wavelet transform segmentation, KMeans algorithm segmentation, and FCM-based segmentation.Sound, artifacts, and inconsistent intensities are all problems for MRIs.These problems meant that the traditional method of image segmentation (2) was not yielding reliable results.First, we presented an improved EnFCM for better segmentation results.A new mathematical model with adjusted parameters for the Modified FCM's intensity and grayscale has been introduced.Furthermore, the calculation time and noise have been reduced by introducing the fuzzy factor and changing the spatial parameter in the EnFCM (Modified FCM) cost function.
Rician noise in MRI images of brain tumors was not diminished despite the fact that the fuzzy factor was used as the cost function of MFCM for noise reduction.It has been suggested that a modified Fuzzy C Means (FCM) can improve Rician noise reduction and tumor diagnosis from an MRI.Mathematical analyses have been developed for the exponential enhancement of the fuzzy factor of the cost function, but they have not been successful in removing Gaussian noise from MRIs.
The segmentation and noise reduction performance of MRI images have been improved by implementing a fast and robust FCM to improve robustness against noise.For better segmentation accuracy, FCM uses median filtering to lower background noise.To minimize salt-and-pepper, Gaussian, and continuous noise by more than 30 %, the FCM requires more parameters and has limited success.
A new MFO is presented in this research (Moth-Flame Optimization).The expected LLRBFNN model for classifying encephalon tumors.Magnetic Resonance (MR) images were segmented using the modified fuzzy c means method (MFCM), and features were removed using the generalized linear classifier fusion method (GLCM).The goal of this research is to use hybrid models and algorithms to classify and segment encephalon tumors in MR images.The collected features were used to inform the suggested LLRBFNN model for benign and malignant tumor migration, which was developed using MFO predictions.

Related work
Literature demonstrates numerous strategies for detecting brain tumors.Charutha and Jayashree made a proposal concerning the malignant Brain Tumor.Their analysis demonstrates that PET and MRI imaging play a significant role in the medical imaging of brain tumors. (6)he work focused on developing a noise-reduction method that recovers useful pictures by means of graylevel co-occurrence matrix (GLCM) features. (7,8)In order to simplify the process and boost efficiency, we used Discrete Wavelet Transform (DWT)-based segmentation to analyze brain tumors.
The suggested method employs morphological filtering, following segmentation, to effectively eradicate background noise.At last, an MRI image classifier based on a probabilistic neural network was developed.It presented a deep cascaded neural network-based automatic segmentation method for Brain vivo Gliomas using MRI data.Separate networks, one for pinpointing tumors and another for classifying cells within tumors, make up this larger network. (9,10)The results from this strategy are extremely plausible.The main strength of this method is that it can merge different parts of tumor images, which is something that other approaches to this problem lack.
To routinely identify brain tumors, introduce a hybrid replica PSO-LLRBFNN Algorithm that achieves an accuracy of 98 % for the ADNI dataset. (11,12) method for encephalon tumor segmentation was suggested based on a hybrid type of approach using FCM, and they reached 90 % noise level precision. (13)When compared to first-order statistical characteristics, (14) it was found that using a wavelet transform and SVM's technique resulted in better prognoses and enhanced clinical factors such as tumor volume, and tumor stage. (15)Using PCA and SVMs, a 94 % relegation accuracy (SVM) was Salud, Ciencia y Tecnología.2022;2(S2):241 2 obtained. (16)oreover, Cui et al. created a localized fuzzy clustering using geodata, claiming an accuracy of 85-95 percent.With the introduction of an active contour model, (17,18 ) a method for medical image segmentation has been developed that can compensate for intensity inhomogeneity.
The use of the Gaussian mixture model (GMM) with MR images for automatic feature extraction and PCA to improve the GMM feature extraction was investigated as well. (19,20)Those studies reported a precision of 93,2 %, a sensitivity of 91,6 %, and a specificity of 97,8 % for brain tumor classification from 3D MR images using an extreme learning machine. (21,22)

METHODS
Our research focuses on the classification of medical images using clustering techniques.The job flow was done in three stages.The study task is divided into the following steps: (i) The segmentation was performed using the MFCM algorithm, and feature extraction was performed using the GLCM feature extraction technique.Additionally, the second phase (ii) provided the eliminated features as input to the MFO-RBFNN (iii) performance evaluation.The Flow Chart of our Proposed Work is shown in Figure 1.To significantly reduce the quantity of estimation conducted during the segmentation process, the MFCM algorithm was introduced to accelerate the gray level picture segmentation method.Modified Fuzzy C-means Algorithm is shown in Figure 2.
Inspired by the transverse orientation navigation techniques of moths, teams like the ones of Huang and Clark, proposed a Moth-Flame Optimization algorithm. (23,24)In this algorithm, the term "population" refers to a collection of moths, and it is believed that flame is the finest treatment for every moth.This algorithm falls under the same principle as PSO.In PSO, each particle's 'p-best' is considered to be its personal best.Similarly, this moth-flame includes one flame per moth, which is regarded as its optimal placement. (25,26)During the iteration, this flame will be updated if a superior option becomes available.Pseudo code of MFO is shown in Figure 3.
The following describes the rationale for initiating the model.1.When modelling samples are limited, the models enable economical interpolation in high-dimensional domains.
3. Because the nodes in the input and hidden layers are equivalent, the total number of nodes required is minimized.

SIMULATION RESULTS
The features were extracted using the GLCM approach, and image segmentation was performed using the Modified FCM technique.The Modified FCM segmentation accuracy outperformed previous approaches in terms of accuracy and noise reduction capability.The proposed MFO-RBFNN model outperformed previously used models in terms of classification accuracy (Figure 4.).We also Selected 12 Features based on Optimum Points: mean radius, mean fractal dimension, area error, smoothness error, compactness error, fractal dimension error, worst texture, worst perimeter, worst smoothness, worst concavity, worst symmetry, worst fractal dimension.

CONCLUSION
The proposed MFO-LLRBFNN model could distinguish between cancerous and benign tumors with high accuracy.In addition, the MFCM method was used for picture segmentation and center selection for the LLRBFNN model, and the MFO algorithm was used to keep those centers up-to-date.We can observe, from our results, the high degrees of categorization accuracy.The suggested "MFO based LLRBFNN" model has improved classification accuracy, although it takes somewhat longer to compute than the "MFO-LLRBFNN" model.Features were extracted from the MR images using the GLCM feature extraction method.Better tumor reclassification outcomes were displayed by the proposed model.As an added bonus, the MFCM has been used to choose the centers of the LLWNNmodel and the MFO method was used to keep those centers up-to-date.When the proposed MFO-based LLWNN model was compared to the "MFO-LLWNN" model, it was discovered that the former required more "computational time," but the latter provided a more accurate classification.Using an optimized data set on a randomly generated forest classifier, a 99 % accuracy was achieved.

Figure 1 . 2 .
Figure 1.Flow Chart of our Proposed Work Figure 2. Modified Fuzzy C-means Algorithm

Figure 3 .
Figure 3. Pseudo code of MFO The RBFNN converges faster and requires fewer neurons than the Multilayer Perceptron (MLP).Local Linear Radial Basis Function Neural Networks (LLRBFN) have been proposed as a way to improve RBFNN performance.

Figure 5
Figure 5 shows the trajectory of finding optimum points by clusters of optimizers at local level ((a) -inside cluster) and at global level ((b)-dataset level).MFO results were based on Calculation of Best Feature.

Figure 5 .
Figure 5. Global and Local objective valueFigure 6 shows the trajectory of fitness between two optimum points by clusters of optimizers at local level ((a) -inside cluster) and at global level ( (a) -dataset level).

Figure 6 .
Figure 6.Global and Local Best Fitness

Figure 7 (Figure 7 .
Figure 7(a) shows the time taken by the optimizer to find an optimum point per epoch, while Figure 7(b) shows a trajectory comparison b/w exploitation and exploration between two optimum points by clusters of optimizers.

Table 1
compares the performance of several classifiers.

Table 1 .
Performance Comparison between Various Classifiers