ORIGINAL

Development of a New Algorithm for Classifying Cerebral Tumours Using MRI Images

Desarrollo de un nuevo algoritmo de clasificación de tumores cerebrales mediante imágenes de resonancia magnética

Bhavna Kaushik Pancholi^¹ *, Pramodkumar Sevantilal Modi¹ *, Nehal Gitesh Chitaliya¹ *

¹The Maharaja Sayajirao University of Baroda. Department of Electrical Engineering. Vadodara, Gujarat, India

Citar como: Kaushik Pancholi B, Sevantilal Modi P, Gitesh Chitaliya N. Development of a New Algorithm for Classifying Cerebral Tumours Using MRI Images. Salud, Ciencia y Tecnología. 2023; 3:434. https://doi.org/10.56294/saludcyt2023434

Received: 20-04-2023 Reviewed: 16-05-2023 Accepted: 23-06-2023 Published: 24-06-2023

Editor: Fasi Ahamad Shaik

ABSTRACT

Healthcare scientists determined how MRI images have indeed been highly beneficial in latest times in the investigation of the recognition and early identification of a brain disease. The main primary stages in analysing the brain MRI pictures are image pre-processing, segmentation, feature extraction, and classification. Among the crucial processes that can evaluate how well brain MRI scans can be classified and ultimately the condition it will indicate is feature extraction and segmentation. In this paper stage wise methods are described. In the first stage (pre-processing stage) different filters; like; median, wiener, anisotropic, non-local means as well as combined filters used. In the pre-processing part, combined wiener and anisotropic filter gives the best result. In the second stage (segmentation stage), multi-thresholding technique – cuckoo search algorithm used using different objective functions; like; ostu, kapur entropy, tsallis entropy and proposed. In the proposed method of the segmentation stage used cuckoo search algorithm using combined ostu and tsallis entopy as an objective function. In the third stage (feature extraction), discrete wavelet transform used and in the fourth stage (classification) support vector machine used. In each stage results are compared using different parameters and we got best output using proposed method.

Keywords: MRI Images; Brain Tumour; Pre-Processing; Image Segmentation; Feature Extraction.

RESUMEN

Los científicos sanitarios han determinado que las imágenes de resonancia magnética han sido muy beneficiosas en los últimos tiempos para la investigación del reconocimiento y la identificación precoz de enfermedades cerebrales. Las principales etapas primarias en el análisis de las imágenes de resonancia magnética del cerebro son el preprocesamiento de imágenes, la segmentación, la extracción de características y la clasificación. Entre los procesos cruciales que pueden evaluar lo bien que se pueden clasificar las imágenes de resonancia magnética del cerebro y, en última instancia, la enfermedad que indicarán, se encuentran la extracción de características y la segmentación. En este artículo se describen métodos por etapas. En la primera etapa (etapa de preprocesamiento) se utilizan diferentes filtros, como la mediana, wiener, anisotrópico, medios no locales, así como filtros combinados. En la parte de preprocesamiento, los filtros wiener y anisotrópicos combinados dan el mejor resultado. En la segunda etapa (etapa de segmentación), la técnica de umbralización múltiple - algoritmo de búsqueda de cuco utilizado utilizando diferentes funciones objetivas; como; ostu, kapur entropía, tsallis entropía y propuso. En el método propuesto de la etapa de segmentación utilizado cuckoo algoritmo de búsqueda utilizando ostu combinado y tsallis entropía como función objetivo. En la tercera etapa (extracción de características), transformada wavelet discreta utilizada y en la cuarta etapa (clasificación) máquina de vectores soporte utilizado. En cada etapa se comparan los resultados utilizando diferentes parámetros y se obtienen los mejores resultados utilizando el método propuesto.

Palabras clave: Imágenes De Resonancia Magnética; Tumor Cerebral; Preprocesamiento; Segmentación De Imágenes; Extracción De Características.

INTRODUCTION

One of the difficult problems in image processing is noise elimination, which deals with the restoration and image improvement of a digital image which has become distorted by noise during gathering, preservation, or dissemination. It is an unavoidable effort to eliminate or at least reduce noise in pictures. Perhaps a small amount of sound is harmful if higher efficiency is needed, as in the medical industry.⁽¹⁾

Figure 1. Block diagram of the proposed system

Figure 1 indicates stage wise block diagram of the proposed system. Stage wise survey given in the section 1.1 to 1.5. Stage wise theoretical aspects given in the section 2. Stage wise results given in the section 3. Conclusion given in the Section 4.

IMAGE ACQUISITION

The primary widely used diagnostic tools are medical images generated from ultrasound (US) imaging, MRI and CT scan and due to its non-invasive, safe, and precise nature. Furthermore, photo collection and equipment may bring up unwanted interruptions like speckle distortion, salt and pepper, Poisson and Gaussian noises. Due to the drastic degradation of optical measurements, like image density and intensity, it is difficult to distinguish among regular and diseased cells in medical studies. Therefore, a necessary pre-processing step to achieve the best evaluation is to denoise medical data without changing limits, modifying essential parts of the images, or harming anatomical traits. Brain tumours and other cerebral abnormalities can be diagnosed, graded, treated, and the effectiveness of the therapies can be evaluated using magnetic resonance imaging (MRI).⁽²⁾ The morphological and physiological features of disorders in the neuraxis are revealed by standard MRI, but its accuracy is constrained. Although with current advancements in image accuracy, stronger electromagnetic fields, and better contrasted agents, standard imaged techniques still have a constrained ability to characterise tissue. Clients will have intrusive brain abnormalities biopsy procedures due to diagnostic uncertainty that carries some risk. The biophysical characteristics of brain tissue may now be statistically measured in vivo using a variety of supplemental MR imaging approaches, improving our understanding of regional variations in the cell micro - structural context. The methods included magnetic resonant spectroscopic, perfusion-weighted imaging, and diffusion-weighted imaging (MRS). The T1- and T2-weighted images are the least popular MRI procedures. Short TE and TR timings are used to create T1-weighted pictures. T1 characteristics of tissue are primarily responsible for determining the contrast and luminance of the picture. On the other hand, longer TE and TR periods are used to create T2-weighted pictures. In order to create this, lengthier TE and TR durations were used. The T2 characteristics of tissue are primarily responsible for determining the clarity and intensity of pictures. T1- and T2-weighted pictures may typically be distinguished simply by examining the CSF. On T1-weighted imaging, CSF is dark, while on T2-weighted imaging, it is bright. The Fluid Attenuated Inversion Recovery phase is a third frequently used technique (Flair).⁽³⁾ The TE and TR timings of the Flair sequence are significantly longer than those of a T2-weighted image. As a result, anomalies are kept light, while the typical CSF fluid is dimmed. This sequence greatly facilitates the distinction among CSF and an abnormal and is highly susceptible to disease. Gadolinium infusion can also be used to produce T1-weighted imaging (Gad). A non-toxic diamagnetic contrast-enhancing compound is called Gad. Gad shortens T1 and alters signal intensity when administered within the scanning. Gad appears highly bright on T1-weighted pictures as a result. Gad enriched pictures are particularly helpful for examining vascular architecture and blood-brain membrane degradation. The purpose of diffusion weighted imaging (DWI) is to identify the erratic motions of water protons. In contrast to the intracellular region, where their migration is severely constrained, water molecules distribute quite easily in the external environment. Distribution, which refers to random motions, immediately becomes constrained in ischemic brain tissue. The sodium-potassium pump malfunctions during ischemia, causing intracellular sodium accumulation. The osmotic gradient then causes water to move from the extracellular to the intracellular area. This causes a very strong signal on the DWI as intracellular water flow is constrained. DWI is a highly accurate approach for identifying severe stroke. In this paper MRI images for brain tumour detection is used.

PRE-PROCESSING

In order to reduce distortion from an MRI image, I. Nagarajan's technique uses a block distinction processing mechanism. It works by adjusting each pixel's weight efficiently, separating edge pixels from noise pixels, and then reducing noise more precisely. Additionally, the method is evaluated on real T1 and T2 MRI scans in longitudinal, sagittal, and coronal viewpoints in addition to T1, T2, and PD feature generated MRI data. This method can be used to predict the processing time of MRI images for clinical clients in the future, and the accuracy of the suggested method can be improved by utilising an angular rotation-based block contrast approach.⁽⁴⁾ The optimal filtering strategy for image pre-processing, which may have a significant impact on the accuracy of tumor cell detection, can be found by comparing Wiener and Median filtration strategies. The result of the Wiener filter determined the average and variable value of the images at a certain phase. The front and back of the threshold were distinguished using the grey-scale method. The deformation in the photographs is successfully removed by the median filter. The findings of the investigation in the previous study do not render the analysis's findings erroneous. The effectiveness of the filters is measured by whether effectively it examines images of blood platelets. The metric used to evaluate the filter's effectiveness was PSNR.⁽⁵⁾

The median filter performed best when the real picture is compared to the de-noised picture, demonstrating that it is a suitable fit for the small picture set employed in the study. For the provided data gathering, the wiener filter was compared to the median filter, and it was found that the median filter functioned 8 % better.

SEGMENTATION

The categorization of brain tumours is a difficult issue in the realm of clinical picture analysis. The current research suggests a hybrid approach that makes use of neuro-physics and convolutional neural networks (NS-CNN).^(6,7,8) It seeks to distinguish between harmless and harmful tumour regions in sections separated from brain pictures. Initially, the neutrosophic group - master greatest fuzzy-sure entropy (NS-EMFSE) method was used to divide the MRI pictures. During the classification phase, CNN collected the characteristics of the fragmented brain pictures and evaluated those utilizing SVM and KNN algorithms. On 80 benign tumors and 80 malign tumors, an operational assessment predicated on 5-fold cross-validation is conducted. The results showed that CNN characteristics have excellent classification performance across a range of classifications.^{(9, 10)}

While modelling findings verified source information with a mean accuracy rate of 95,62 %, actual findings show that CNN characteristics demonstrated superior categorization efficiency with SVM. By identifying brain tumors as harmless and malignant, the primary objective of this article is to develop an effective automated brain tumor separation method. NS-EMFSE was used to separate brain tumors. Alexnet collected the characteristics of the divided pictures using CNN structures, and SVM and KNN classifiers being used to determine their classification. The deep learning technique that uses feed-forward layers is CNN. The application utilized Matlab 2017b and the MatConvnet library. SVM classifier produced the best results, scoring 95,62 %.^(11,12)

If more photos are utilized in the collection, it is predicted that this reliability percentage would rise. Research on segmentation and classification is among the least often discussed subjects in picture analysis. The renowned and effective segmentation and classification techniques CNN and Neutrosophy can be used, and this could significantly advance picture recognition. I intend to look into how distinct Neutrosophic methods using various CNN topologies affect classification reliability in upcoming research. Depending on a convolutional neural network with neutrosophic professional maximum fuzzy sure entropy, a brain tumor identification algorithm.

FEATURE EXTRACTION

Every photo software must use feature extraction and picture pre-processing methodologies. To guarantee the effectiveness of the following stages, these approaches should have a very large precision and resolution rate. However, these strategies' importance is frequently overlooked, that yields subpar outcomes. The significance of those methods is addressed in this research within the perspective of categorization and segmentation of Magnetic Resonance (MR) brain images. The removal of the skull section around the brain cells is accomplished in this research using appropriate pre-processing procedures. In the study, texture-based feature extracting methods are further demonstrated.⁽¹³⁾

The research findings are examined in respect of feature extracting strategies and separation effectiveness for pre-processing. In this study, the converging probability of different techniques is additionally covered. The outcomes of the experiments are encouraging for the suggested strategies. This research validates the value of feature extraction and pre-processing approaches. The presented methods have a very excellent reliability rate. The fact that the time commitment is relatively minimal shows that these strategies are doable. Therefore, these methods can be applied to real medical situations in which precision and resolution rate are crucial.

CLASSIFICATION

Brain tumors grow when cells arise abnormally. It is one of the leading causes of adult mortality worldwide. Early brain tumor discovery may avert countless of fatalities. Magnetic Resonance Imaging (MRI) earlier brain tumor detection can improve client life. The tumor is very readily seen in an MRI, which aids in the course of additional therapy. The research tries to identify tumors earlier on. Methodologies: In the paper, the input slices are enhanced and de-noised using a Weiner filter using several wavelet bands. Using the help of Potential Field (PF) categorization, tumor pixel subgroups are discovered. Additionally, in Fluid Attenuated Inversion Recovery (Flair) and T2 MRI, the tumor zone is isolated using a worldwide threshold and several statistical morphological techniques.

Local Binary Pattern and Gabor Wavelet Transform characteristics are combined for precise categorization. Results: The PSNR, MSE and SSIM are used to test the suggested technique. The outcomes are 76,38, 0,037, and 0,98 on T2 and 76,2, 0,039, and 0,98 on Flair, accordingly. Using the basis of pixels, distinct characteristics, and fused functionalities, the classification outcome has been assessed. The suggested method is compared with ground fact slices at the pixel level and validated in respect of foreground, background, and error region and pixel clarity.⁽¹⁴⁾

THEORETICAL ASPECT OF TECHNIQUES

In the pre-processing part, there are several filters used for noise removal, like; Median, Wiener, Anisotropic Diffusion filter, Non Local Means, etc.^(15,16)Using an estimate of the necessary noise-free signal, the Wiener filter is frequently used in this to reduce the amount of distortion in a scan. It uses a statistical method. The Wiener filter is popular and produces high-quality results when compared to other filters because of how quickly and easily it can be implemented. Additionally, combining various filters yields results that are more accurate. In this paper different combination of the combined filters are used for pre-processing of an image; like; combined Weiner and anisotropic, combined Weiner and median, combined Weiner and non-local means. In this combined Weiner and anisotropic gives best output.

There are so many techniques are used for image segmentation. In this thresholding is the one of the technique used for image segmentation.⁽¹⁷⁾There are two types of thresholding; one is bi-level thresholding and other is multi-level thresholding.⁽¹⁸⁾Compare to the bi-level thresholding technique, multi-level thresholding techniques gives good performance. There are so many different algorithm are used in the multi-level thresholding techniques.⁽³⁾ One of the algorithms is cuckoo search algorithm. In the cuckoo search algorithm, different objective functions are used for optimum output.⁽¹⁹⁾

CUCKOO SEARCH (CS) ALGORITHM

The cuckoo search algorithm was proposed by Yang.⁽¹⁰⁾ and is a naturalistic method. The CS imitates the cuckoo's egg-laying operation. Cuckoos typically lay their fertilised eggs in host nests in the hopes that they would be nurtured by substitute parents in spring. The host may recognise that the eggs in their nests are not their own occasionally. In these situations, either the entire nest is thrown out or the foreign eggs are removed from the nests. The CS optimization algorithm is generally based on the following three principles:

1. It's interesting to note that each cuckoo bird only lays one egg at a time, which it randomly inserts in the nest of a host bird.

2. Typically, the best nests with top-notch eggs are passed down to the following generations.

3. There are a fixed number of host nests available. With probability p_α, the host bird finds foreign eggs with a probability that ranges from 0 to 1. Keep in mind that the best nests are chosen for the subsequent calculations. For the sake of simplicity, principle 3 can be described as follows: with probability p_α, new nests will replace the existing n nests.

The CS method can be distilled into the following three ideas based on these three principles:

A Lévy light is carried out while producing new solution x_i^t+1 for cuckoo I⁽¹⁰⁾

x_i^t+1= x_i^t+ α₀ (x_i^t-x_best) ⊕ Levy(λ)…….. (1)

Where x_best denotes the current best solution, Element-wise multiplication, and α₀ is the step size α₀> 0.

Levy flights are selected using a Levy distribution, which is given by:

Levy(λ) ∼u=t^-^λ,(1<λ≤3)……… (2)

Based on the breeding behaviour of several cuckoo species, we have developed a new metaheuristic called Cuckoo Search in conjunction with L'evy flights in this work. Show that CS is better than other metaheuristic algorithms based on the literature review. Studying multiobjective optimization applications is a simple extension of the possibly effective optimization method. Levy step size and x_best are the main challenges of the cuckoo search algorithm. x_best is found using optimization. Different methods are used to maximize the function, like; using Ostu’s method, using Kapur entropy method, using Tsallis entropy method, etc.⁽²⁰⁾

PROPOSED CUCKOO SEARCH (CS) ALGORITHM

Otsu's presumption of binary classes is its major drawback: The grayscale histogram is divided into two classes. However, in real-world segmentation problems we most generally deal with images having more than two classes of segments. According to the literature review, employing the Otsu approach for CS results in lower PSNR values than utilising Kapur's entropy. Because Tsallis entropy is non-extensive, when two identical systems unite, the combined system's entropy is not equal to the sum of the entropies of its subsystems. Compare to CS using Kapur’s entropy method Tsallis entropy gives higher PSNR values and other parameters. The long-range correlations in an image can be described using the non-extensive entropy technique.⁽²⁰⁾The range of its use is constrained by the fact that, like other entropy-based algorithms, it is still extremely sensitive to the disturbance of signals. For small target extraction, the Otsu method is more reliable but less precise. The benefits of the two can thus be combined to create a new algorithm with a broader range of applications. We proposed new method, combined Otsu and Tsallis entropy as an objective function to find the x_best.

The long-range correlations in an image can be described using the non-extensive entropy technique. The range of its use is constrained by the fact that, like other entropy-based algorithms, it is still extremely sensitive to the disturbance of signals. For small target extraction, the Otsu method is more reliable but less precise. The benefits of the two can thus be combined to create a new algorithm with a broader range of applications. Noting that information redundancy now governs the non-extensive parameter q in Tsallis entropy and cannot be set arbitrarily.

A new objective function is denoted by:

μ(t)=S_q^a+b– (σ_W² )^1-q…….. (3)

q > 0 needs to be met in order to maintain the concavity of Tsallis entropy.⁽¹⁰⁾ In contrast, q < 1 is referred to as superextensivity, which raises the system's overall entropy in compared to the extensive case (q = 1).⁽²¹⁾ Practically speaking, practically all kinds of images display the superextensivity feature.⁽²²⁾ Consequently, 0 < q < 1 can be the appropriate range for the non-extensive parameter. We can see that each of the two strategies is designed to maximise the objective functions... Equations 3's goal is to increase the objective function.

t^*=Argmax{μ(t)} …….. (4)

With the range of q stated above, equation 4 yields the ideal threshold. The profile of each peak is the normalised q-Gaussian distribution function for a synthetic image with a bimodal histogram distribution, as shown in figure 2.⁽⁶⁾ We can see that the best threshold for the Otsu algorithm and the Tsallis entropy algorithm is the valley gray-level between the two peaks, which exactly matches the outcome of equation 4.⁽²³⁾ There is no proof that the result of ostu and tsallis entropy coincide for other natural photographs with an arbitrary histogram distribution, however equation 3 shows a trade-off between them and equation 4 may produce an appropriate proposal. It should be noted that S_q^a+b and σ_W² have extremely different magnitudes in the histogram of figure 2. Both of them are functions of threshold t, as illustrated in figure 3. The Otsu method, however, completely suppresses the Tsallis entropy algorithm's outputs for any conceivable threshold.⁽²⁴⁾Consequently, it is inappropriate to directly combine S_q^a+b and σ_W².

Figure 2. Normalized histogram distribution

Figure 3. Objective functions of the Ostu and Tsallis algorithms

The magnitude of can be revised using the q-exponential function⁽²⁰⁾ to minimise the effects of the magnitude difference. With a continuous probability distribution function, Tsallis entropy is defined as:

…….. (5)

Where the probability density of the normalised gray-level value is denoted by p(x). The appropriate probability distribution for a system with non-extensive q-entropy can be expressed as the q-Gaussian function⁽²⁵⁾,

…….. (6)

If Zq is the partition function to maintain the probability normalisation requirement and is the variance of x,

…….. (7)

Where factorial (k - 1)! will be reached from the Gamma function Γ(k) assuming k is a number. Adding p(x) to equation 18 results in:

…….. (8)

Where:

…….. (9)

Is the integration constant for a given value of q. According to the non-extensivity of Tsallis entropy, if If p_a and p_b are two identical q-Gaussian distribution functions, the total entropy can be expressed as follows:

….. (10)

Substituting = = into equation 23 yields:

……. (11)

Therefore, the magnitude of is comparable with at the proper range of q, and the rationality of equation 3 is shown.

Pre-processing and Segmentation image samples are shown in figures 8 and 9. The Weiner Filter, Anistropic Filter and Weiner Filter & Anistropic Filter Pictorial Presentation is displayed in the front row of figures 5 and 6. These filters are described theoretically in the section 3 along with their parameters like PSNR, MSE, RMSE & UQI with their equations. The segmentation output, which includes the Cuckoo Ostu Threshold, Cuckoo Kapur Threshold, Cuckoo Tsallis Threshold and Cuckoo Otsu & Tsallis Threshold is vividly represented. The proposed research demonstrates that when compared to various individual and combined filters, the Cuckoo Otsu & Tsallis entropy combination produces outstanding outcomes.

FEATURE EXTRACTION

The description of parameters implemented for 7 images is given below⁽⁸⁾

MEAN

The intensity of a grey magnitude picture is typically measured statistically using the term mean.⁽⁹⁾

……. (16)

STANDARD DEVIATION

This variable displays the degree of "diffusion" from the median or anticipated intensity of the pixel. The significant standardized variation signifies that the data units are dispersed relatively randomly, whereas a low standardized variation indicates that the data units have a tendency to be near the mean.⁽¹⁴⁾

The Standard Deviation is given by:

…… (17)

VARIANCE

It evaluates neighbouring distinction and sums up the histogram dispersion.⁽²¹⁾The divergence and dispersion from the average are greater the more the variance.

……. (18)

ROOT MEAN SQUARE ERROR

It calculates the discrepancy among the observed value that a system or classifier anticipated and the real value.

….. (19)

…… (20)

ENTROPY

This relates to the amount of energy which is constantly dissipated to heat whenever a process or structural transition takes place; the idea derives from thermodynamics. When grey values are dispersed evenly, perhaps with identical possibilities, a picture's entropy is at its highest. Photos with minimal entropy have little content and poor picture resolution, whereas pictures with greater levels have more data and better features. Entropy can never be restored to be put to productive usage. As a result, the word might be interpreted as the degree of irreparable disarray or instability. The entropy of a grayscale picture can be described as:

…. (21)

SKEWNESS

It serves as a measure for symmetrical or, rather specifically, for asymmetry.⁽²¹⁾When a distributed or set of data appears identical to the left and right of the centre, it is said to be symmetrical.

…… (22)

KURTOSIS

It evaluates how heavy or light-tailed the information are in comparison to a statistical allocation.

…….. (23)

ENERGY

It is consistency or angular second moment (ASM). The value increases with the homogeneity of the picture. The assumption is that the picture is a steady picture if energies is equivalent to 1.⁽²¹⁾

…… (23)

CONTRAST

Brightness or grey-level differences among the standard pixel and its neighbour are measured as contrast. Contrast in the actual globe is created by the contrast between one element as well as other things in the identical range of sight in colour and intensity. The unit is located on the diagonal therefore x- y = 0 if x and y are identical. Assuming a value of 0, such data indicate pixels that are identical to its neighbour in every way. There is a slight difference and the value is 1 if x and y vary by 1. The contrast is rising and the magnitude is four if x and y are varied by two. As (x,y) rises, the weights tend to rise rapidly.⁽²¹⁾

…….. (25)

CORRELATION

The co-occurrence matrix grey level elements linear dependence is demonstrated by the correlation characteristic. It shows how closely linked a baseline pixel is to its neighbour’s. Here 0 indicates no connection and 1 indicates an ideal one.

……. (26)

INVERSE DIFFERENT MOMENT (IDM)

IDM is typically referred to as homogeneity which evaluates the regional homogeneity of a picture. The assessments of the dispersion of GLCM component’s proximity to the GLCM diagonal are obtained by the IDM characteristic. Its value obtained is the opposite of a Contrast weight having values falling off the diagonal progressively.

…… (27)

HOMOGENEITY

It analyses the resemblance of pixels. The homogeneity of a diagonally grey layer co-occurrence vector is 1.⁽²¹⁾ If just little modifications are made to localize texturing, it expands in size.

……. (28)

Table 1 presents the Overview of the parameters for 7 scans in table format. The parameters covered are Contrast, Correlation, Energy, Homogeneity, Mean, and Standard Deviation. Table 2 also presents the Overview of the other parameters for 7 scans in table format. The parameters covered are Entropy, RMS, Variance, Smoothness, Kurtosis, and Skewness & Inverse Different Moment (IDM).

Table 1. Overview of the parameters for 7 scans in table format
Image	Contrast	Correlation	Energy	Homogeneity	Mean	Standard Deviation
1	0,2539	0,1014	0,7610	0,9329	0,0017	0,0892
2	0,2258	0,1529	0,7689	0,9364	0,0017	0,0892
3	0,2152	0,1143	0,7526	0,9326	0,0048	0,0896
4	0,3167	0,1297	0,7949	0,9395	0,0075	0,0899
5	0,243	0,0943	0,7488	0,9300	0,0050	0,0897
6	0,23	0,0871	0,7513	0,9301	0,0046	0,0895
7	0,2364	0,1479	0,7429	0,9287	0,0046	0,0895

Table 2. Overview of the parameters for 7 scans in table format
Image	Entropy	RMS	Variance	Kurtosis	Skewness	Inverse Different Moment (IDM)
1	2,9641	0,0898	0,0081	7,8474	0,5534	0,1548
2	3,0447	0,0897	0,0082	7,2954	0,3964	0,6042
3	3,7365	0,0868	0,0081	5,8455	0,4031	1,5651
4	3,1611	0,0892	0,0080	13,499	1,3523	0,8302
5	3,614	0,0892	0,0081	6,052	0,5247	1,1661
6	3,7295	0,0848	0,0082	5,6958	0,3829	0,5801
7	3,5494	0,0886	0,0081	6,573	0,6144	0,3837

In table 8, five different parameters using confusion matrix are found for different methods. In first method, in the segmentation part, multi-thresholding cuckoo algorithm using ostu as an objective function used. In second method, in the segmentation part, multi-thresholding cuckoo algorithm using kapur entropy as an objective function used. In third method, in the segmentation part, multi-thresholding cuckoo algorithm using Tsallis entropy as an objective function used in the proposed multi-thresholding cuckoo algorithm combined ostu and Tsallis entropy used as an objective function that gives best output for all parameters.

CONCLUSION

The research activity consists of several stages, including Pre-processing, Segmentation, Feature Extraction and Classification. Individual and combinations of filters such as the Median, Weiner, Anisotropic, NLM, Weiner and NLM, Weiner and Anisotropic, and Weiner and Median have been described for the pre-processing process. Weiner and anisotropic filtering together produced the best results. Various thresholding, entropy, and evolutionary techniques, such as Two-level thresholding, Multilevel thresholding, Otsu’s method, Kapur’s entropy method, Tsallis entropy method & Cuckoo search (CS) algorithm, Lévy flight modelling are detailed in segmentation. It is observed that the Cuckoo Otsu & Tsallise Threshold combination delivers outstanding outcome in segmentation. In Feature Extraction Discrete Wavelet Transform (DWT) is used. One seventy images were considered for MRI scan and numerous parameters were discussed. Since Segmentation cannot generate an accurate outcome and hence Feature extraction is utilized. Based on textive feature of an image the parameters are compared to trained data and then its classification is done. Regarding Classification methodology Support vector machine (SVM) is described. The Confusion matrix and various filter combinations are deployed for detecting tumors. The parameters like Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) & Accuracy (Acc) of combination of filters are compared with help of True Positive, True Negative, and False Positive & False Negative. The combination of (Weiner + Anistropic) + Cuckoo combined {Ostu+ Tsallis} + DWT + SVM deliver excellent result. The proposed methodology reflects the effective way to identify the nature of brain tumors along with adequate MRI scan.

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CONFLICTS OF INTEREST

none.

FINANCING

No financing.

AUTHORSHIP CONTRIBUTION

Conceptualization: Bhavna Kaushik Pancholi, Pramodkumar Sevantilal Modi, Nehal Gitesh Chitaliya.

Research: Bhavna Kaushik Pancholi, Pramodkumar Sevantilal Modi, Nehal Gitesh Chitaliya.

Writing - proofreading and editing: Bhavna Kaushik Pancholi, Pramodkumar Sevantilal Modi, Nehal Gitesh Chitaliya.

Table 3. Confusion Matrix
Total Images		Predicted
		With Tumor	NO Tumor
Actual	With Tumor	TP	FN
	NO Tumor	FP	TN

Table 4. Confusion Matrix for using [(Weiner + Anistropic) + Cuckoo Otsu + DWT + SVM]
Total Images – 170 Tumor Images – 110 No Tumor Images - 60		Predicted
		With Tumor	NO Tumor
Actual	With Tumor	TP (100)	FN (10)
	NO Tumor	FP (07)	TN (53)

Table 5. Confusion Matrix for using [(Weiner + Anistropic) + Cuckoo Kapur + DWT + SVM]
Total Images – 170 Tumor Images – 110 No Tumor Images - 60		Predicted
		With Tumor	NO Tumor
Actual	With Tumor	TP (103)	FN (07)
	NO Tumor	FP (05)	TN (55)

Table 6. Confusion Matrix for using [(Weiner + Anistropic) + Cuckoo Tsallis + DWT + SVM]
Total Images – 170 Tumor Images – 110 No Tumor Images - 60		Predicted
		With Tumor	NO Tumor
Actual	With Tumor	TP (106)	FN (04)
	NO Tumor	FP (04)	TN (56)

Table 7. Confusion Matrix for using [(Weiner + Anistropic) + Cuckoo combined {Ostu + Tsallis } + DWT + SVM]
Total Images – 170 Tumor Images – 110 No Tumor Images - 60		Predicted
		With Tumor	NO Tumor
Actual	With Tumor	TP (108)	FN (02)
	NO Tumor	FP (03)	TN (57)

Table 8. Different Methods with their respective parameters for tumor detection
Sr. No	Methods	Se	Sp	PPV	NPV	Acc
1	(Weiner + Anistropic) + Cuckoo Otsu+ DWT + SVM	0,909	0,8833	0,9345	0,8412	0,900
2	(Weiner + Anistropic) + Cuckoo Kapur + DWT + SVM	0,936	0,9166	0,9537	0,8870	0,929
3	(Weiner + Anistropic) + Cuckoo Tsallis + DWT + SVM	0,9636	0,9333	0,9636	0,9333	0,952
4	Proposed Method	0,981	0,9500	0,9729	0,9661	0,970

RESULT ANALYSIS

CONFLICTS OF INTEREST

none.

FINANCING

No financing.

AUTHORSHIP CONTRIBUTION