Survival estimation of oral cancer using fuzzy deep learning.

BACKGROUND: Oral cancer is a deadly disease and a major cause of morbidity and mortality worldwide. The purpose of this study was to develop a fuzzy deep learning (FDL)-based model to estimate the survival time based on clinicopathologic data of oral cancer. METHODS: Electronic medical records of 581 oral squamous cell carcinoma (OSCC) patients, treated with surgery with or without radiochemotherapy, were collected retrospectively from the Oral and Maxillofacial Surgery Clinic and the Regional Cancer Center from 2011 to 2019. The deep learning (DL) model was trained to classify survival time classes based on clinicopathologic data. Fuzzy logic was integrated into the DL model and trained to create FDL-based models to estimate the survival time classes. RESULTS: The performance of the models was evaluated on a test dataset. The performance of the DL and FDL models for estimation of survival time achieved an accuracy of 0.74 and 0.97 and an area under the receiver operating characteristic (AUC) curve of 0.84 to 1.00 and 1.00, respectively. CONCLUSIONS: The integration of fuzzy logic into DL models could improve the accuracy to estimate survival time based on clinicopathologic data of oral cancer.

Fuzzy machine learning logic utilization on hormonal imbalance dataset.

In this research work, a novel fuzzy data transformation technique has been proposed and applied to the hormonal imbalance dataset. Hormonal imbalance is ubiquitously found principally in females of reproductive age which ultimately leads to numerous related medical conditions. Polycystic Ovary Syndrome (PCOS) is one of them. Treatment along with adopting a healthy lifestyle is advised to mitigate its consequences on the quality of life. The biological dataset of hormonal imbalance "PCOS" provides limited results that is whether the syndrome is present or not. Also, there are input variables that contain binary responses only, to deal with this conundrum, a novel fuzzy data transformation technique has been developed and applied to them thus leading to their fuzzy transformation which provides a broader spectrum to diagnose PCOS. Due to this, the output variable has also been transformed. Hence, a novel fuzzy transformation technique has been employed due to the limitation of the dataset leading to the transition of binary classification output into three classes. An adaptive fuzzy machine learning logic model is developed in which the inference of the transformed biological dataset is performed by the machine learning techniques that provide the fuzzy output. Machine learning techniques have also been applied to the untransformed biological dataset. Both implementations have been compared by computation of the relevant metrics. Machine learning employment on untransformed biological dataset provides limited results whether the syndrome is present or absent however machine learning on fuzzy transformed biological dataset provides a broader spectrum of diagnosis consisting of a third class depicting that PCOS might be present which would ultimately alert a patient to take preventive measures to minimize the chances of syndrome development in future.

Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis.

Cervical cancer is a prevalent malignant tumor within the female reproductive system and is regarded as a prominent cause of female mortality on a global scale. Timely and precise detection of various phases of cervical cancer holds the potential to substantially enhance both the rate of successful treatment and the duration of patient survival. Fluorescence spectroscopy is a highly sensitive method for detecting the biochemical changes that arise during cancer progression. In our study, fluorescence spectral data is collected from a diverse group of 110 subjects. The potential of the scattering transform technique for the purpose of cancer detection is explored. The processed signal undergoes an initial decomposition into scattering coefficients using the wavelet scattering transform (WST). Subsequently, the scattering coefficients are subjected to computation for fuzzy entropy, dispersion entropy, phase entropy, and spectral entropy, for effectively characterizing the fluorescence spectral signals. These combined features generated through the proposed approach are then fed to 1D convolutional neural network (CNN) classifier to classify them into normal, pre-cancerous, and cancerous categories, thereby evaluating the effectiveness of the proposed methodology. We obtained mean classification accuracy of 97% using 5-fold cross-validation. This demonstrates the potential of combining WST and entropic features for analyzing fluorescence spectroscopy signals using 1D CNN classifier that enables early cancer detection in contrast to prevailing diagnostic methods.

Prediction on nature of cancer by fuzzy graphoidal covering number using artificial neural network.

Predicting the chances of various types of cancers for different organs in the human body is a typical decision-making process in medicine and health. The signaling pathways have played a vital role in increasing or decreasing the possibility of the deadliest disease, cancer. To combine the pathways concept and ambiguity in the prediction techniques of such diseases, we have used the proposed research on fuzzy graphoidal covers of fuzzy graphs in this paper. Determining a path with uncertainty and shortest length is a challenging topic of graph theory, and a collection of such shortest paths maintaining specific conditions is defined as a fuzzy graphoidal cover for a fuzzy graph. Also, we have defined fuzzy graphoidal covering number as a new parameter, reflecting the measure of coverage by fuzzy graphoidal covering set in a system. Afterwards, some important characterizations of the fuzzy graphoidal covering number are established with justified proof. Also, specific limit values of this number are provided for particular cases. Then, we developed an efficient algorithm for finding the defined covering set with its space and time complexity. The findings of this proposed study have been composed with an artificial neural network to model a strong tool for resolving an essential issue of medical sciences, the prediction of cancer type in the human body. We have analyzed two types of neural networks such as one one-dimensional and two-dimensional specification, for clarity of the obtained results. Also, we have found out the most possible cancer type is breast cancer from the data of our considered case study as a concluding statement for any decision-maker in the field of health sciences. Finally, sensitivity analysis and comparative study have been done to show the stability of our proposed work.

The use of social simulation modelling to understand adherence to diabetic retinopathy screening programs.

The success of screening programs depends to a large extent on the adherence of the target population, so it is therefore of fundamental importance to develop computer simulation models that make it possible to understand the factors that correlate with this adherence, as well as to identify population groups with low adherence to define public health strategies that promote behavioral change. Our aim is to demonstrate that it is possible to simulate screening adherence behavior using computer simulations. Three versions of an agent-based model are presented using different methods to determine the agent's individual decision to adhere to screening: (a) logistic regression; (b) fuzzy logic components and (c) a combination of the previous. All versions were based on real data from 271,867 calls for diabetic retinopathy screening. The results obtained are statistically very close to the real ones, which allows us to conclude that despite having a high degree of abstraction from the real data, the simulations are very valid and useful as a tool to support decisions in health planning, while evaluating multiple scenarios and accounting for emergent behavior.

Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation.

Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.

Comparison of hybrid RNA-based models to predict the degradation and mineralization of the microcontaminant hormone 17ß-estradiol.

New alternatives for effluent decontamination, such as electrochemical oxidation, are being developed to provide adequate removal of endocrine disruptors such as 17ß-estradiol in wastewater. In this study, data-driven models of response surface methodology, artificial neural networks, wavelet neural networks, and adaptive neuro-fuzzy inference system will be used to predict the degradation and mineralization of the microcontaminant hormone 17ß-estradiol through an electrochemical process to contribute to the treatment of effluent containing urine. With the use of different statistical criteria and graphical analysis of the correlation between observed and predicted data, it was possible to conduct a comparative analysis of the performances of the data-driven approaches. The results point to the superiority of the adaptive neuro-fuzzy inference system (correlation coefficient, R2, ranged from 0.99330 to 0.99682 for TOC removal and from 0.95330 to 0.99223 for the degradation of the hormone 17ß-estradiol) techniques over the others. The remaining results obtained with the other metrics are consistent with this analysis.

Using Fuzzy Mathematical Model in the Differential Diagnosis of Pancreatic Lesions Using Ultrasonography and Echographic Texture Analysis.

Malignant tumors of the pancreas are the fourth leading cause of cancer-related deaths. This is mainly because they are often diagnosed at a late stage. One of the challenges in diagnosing focal lesions in the pancreas is the difficulty in distinguishing them from other conditions due to the unique location and anatomy of the organ, as well as the similarity in their ultrasound characteristics. One of the most sensitive imaging modalities of the pancreas is endoscopic ultrasonography. However, clinicians recognize that EUS is a difficult and highly operator-dependent method, while its results are highly dependent on the experience of the investigator. Hybrid technologies based on artificial intelligence methods can improve the accuracy and objectify the results of endosonographic diagnostics. Endoscopic ultrasonography was performed on 272 patients with focal lesions of the pancreatobiliary zone, who had been treated in the surgical section of the Kursk Regional Clinical Hospital in 2014-2023. The study utilized an Olympus EVIS EXERA II video information endoscopic system, along with an EU-ME1 ultrasound unit equipped with GF UM160 and GF UC140P-AL5 echo endoscopes. Out of the focal formations in the pancreatobiliary zone, pancreatic cancer was detected in 109 patients, accounting for 40.1% of the cases. Additionally, 40 patients (14.7%) were diagnosed with local forms of chronic pancreatitis. The reference sonograms displayed distinguishable focal pancreatic pathologies, leading to the development of hybrid fuzzy mathematical decision-making rules at the South-West State University in Kursk, Russian Federation. This research resulted in the creation of a fuzzy hybrid model for the differential diagnosis of chronic focal pancreatitis and pancreatic cancer. Endoscopic ultrasonography, combined with hybrid fuzzy logic methodology, has made it possible to create a model for differentiating between chronic focal pancreatitis and pancreatic ductal adenocarcinoma. Statistical testing on control samples has shown that the diagnostic model, based on reference endosonograms of the echographic texture of pancreatic focal pathology, has a confidence level of 0.6 for the desired diagnosis. By incorporating additional information about the contours of focal formations obtained through endosonography, the reliability of the diagnosis can be increased to 0.9. This level of reliability is considered acceptable in clinical practice and allows for the use of the developed model, even with data that is not well-structured.

Fuzzy Controller Implemented for Movement of a Tendon-Driven 3D Robotic Lumbar Spine Mechanism.

Notable efforts have been devoted to the development of biomechanical models of the spine, so the development of a motion system to control the spine becomes expressively relevant. This paper presents a fuzzy controller to manipulate the movement of a 3D robotic mechanism of the lumbar spine, which is driven by tendons. The controller was implemented in Matlab/Simulink R2023a software, MathWorks (Brazil), considering mathematical modeling based on the Lagrangian methodology for simulating the behavior of the lumbar spine dynamic movement. The fuzzy controller was implemented to perform movements of two joints of the 3D robotic mechanism, which consists of five vertebrae grouped into two sets, G1 and G2. The mechanism's movements are carried out by four servomotors which are driven by readings from two sensors. For control, the linguistic variables of position, velocity and acceleration were used as controller inputs and the torque variables were used for the controller output. The experimental tests were carried out by running the fuzzy controller directly on the 3D physical model (external to the simulation environment) to represent flexion and extension movements analogous to human movements.

Application of nanotechnology in breast cancer screening under obstetrics and gynecology through the use of CNN and ANFIS.

Breast cancer is the leading reason of death among women aged 35 to 54. Breast cancer diagnosis still presents significant challenges, and preventing the disease's most severe symptoms requires early detection. The role of nanotechnology in the tumor-treatment has recently attracted a lot of interest. In cancer therapies, nanotechnology plays a major role in the medication distribution process. Nanoparticles have the ability to target tumors. Nanoparticles are favorable and maybe preferable for usage in tumor detection and imaging due to their incredibly small size. Quantum dots, semiconductor crystals with increased labeling and imaging capabilities for cancer cells, are one of the particles that have received the most research attention. The design of the research is cross-sectional and descriptive. From April through September of 2020, data were gathered at the State Hospital. All pregnant women who came to the hospital throughout the first and second trimesters of the research's data collection were included in the study population. 100 pregnant women between the ages of 20 and 40 who had not yet had a mammogram comprised the research sample. 1100 digitized mammography images are included in the dataset, which was obtained from a hospital. Convolutional neural networks (CNN) were used to scan all images, and breast masses and mass comparisons were made using the malignant-benign categorization. The adaptive neuro-fuzzy inference system (ANFIS) then examined all of the data obtained by CNN in order to identify breast cancer early using inputs based on the nine different inputs. The precision of the mechanism used in this technique to determine the ideal radius value is significantly impacted by the radius value. Nine variables that define breast cancer indicators were utilized as inputs to the ANFIS classifier, which was then used to identify breast cancer. The parameters were given the necessary fuzzy functions, and the combined dataset was applied to train the method. Testing was initially performed by 30% of dataset that was later done with the real data obtained from the hospital. The accuracy of the results for 30% data was 84% (specificity =72.7%, sensitivity =86.7%) and the results for the real data was 89.8% (sensitivity =82.3%, specificity =75.9%), respectively.

Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy inference system (ANFIS).

In chest computed tomography (CT), the breasts located within the scan range receive a substantial radiation dose. Due to the risk of breast-related carcinogenesis, analyzing the breast dose for justification of CT examinations seems necessary. The main goal of this study is to overcome the limitations of conventional dosimetry methods, such as thermoluminescent dosimeters (TLDs) by introducing the adaptive neuro-fuzzy inference system (ANFIS) approach. In this study, the breast dose of 50 adult female patients who underwent chest CT examinations was measured directly by TLDs. Then, the ANFIS model was developed with four inputs including dose length product (DLP), volumetric CT dose index (CTDIvol), total mAs, and size-specific dose estimate (SSDE), and one output (TLD dose). Additionally, multiple linear regression (MLR) as a traditional prediction model was used for linear modeling and its results were compared with the ANFIS. The TLD reader results showed that the breast dose value was 12.37 ± 2.46 mGy. Performance indices of the ANFIS model, including root mean square error (RMSE) and correlation coefficient (R), were calculated at 0.172 and 0.93 for the testing dataset, respectively. Also, the ANFIS model had superior performance in predicting the breast dose than the MLR model (R = 0.805). This study demonstrates that the proposed ANFIS model is efficient for patient dose prediction in CT scans. Therefore, intelligence models such as ANFIS are suggested to estimate and optimize patient dose in CT examinations.

Classification of skin cancer stages using a AHP fuzzy technique within the context of big data healthcare.

BACKGROUND AND OBJECTIVES: Skin conditions in humans can be challenging to diagnose. Skin cancer manifests itself without warning. In the future, these illnesses, which have been an issue for many, will be identified and treated. With the rapid expansion of big data healthcare framework summarization and precise prediction in early stage skin cancer diagnosis, the fuzzy AHP technique produces the best results in both of these fields. Big data is a potent technology that enhances the standard of research and generates better results more rapidly. This essay gives a way to group the stages of skin cancer treatment based on this information. The combination of support vector machine multi-class classification and fuzzy selector with radial basis function-based binary migration classification of virtual machines is put through a number of experiments. The connections have been categorized. ANALYSIS METHOD: These examinations have determined whether the tumors are malignant or benign and how malignant they are. The images of spots on the skin acquired from laboratory images make up the data set used for processing. We have talked about how to handle and process large datasets in the area of classification using MATLAB, like skin spot images. FINDINGS: Our technique outperforms competing approaches by maintaining stability even as the size of the data set grows rapidly and with little error. In comparison to other methods, the suggested approach meets the accuracy criterion for correct classifications with a score of 90.86%. As a result, the proposed solution is viewed as a potentially useful tool for identifying mass stages and categorizing skin cancer severity.

Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization.

Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps choose the proper treatment. In this paper, an online method with an innovative optimization approach as well as a new and fast tumor segmentation method is proposed for the fully automated grading of brain tumors in magnetic resonance (MR) images. First, the tumor is segmented based on two characteristics of the tumor appearance (intensity and edges information). Second, the features of the tumor region are extracted. Then, the online support vector machine with the kernel (OSVMK) by dynamic fuzzy rule-based optimization of the parameters is used for the grading of tumors. The performance evaluation of the proposed tumor segmentation method was performed by manual segmentation using similarity criteria. Also, tumor grading results compared the proposed online method, the conventional online method, and the batch SVM with the kernel (batch SVMK) in terms of accuracy, precision, recall, specificity, and execution times. The segmentation results show a good correlation between the tumor segmented by the proposed method and by experts manually. Also, the grading results based on the accuracy, precision, recall, and specificity, 95.20%, 97.87%, 96.48%, and 96.45%, respectively, indicate the acceptable performance of the proposed method. The execution times of the introduced online method are much less than the batch SVMK. The method demonstrates the potential of fully automated tumor grading to provide a non-invasive diagnosis in order to determine the treatment strategy for the disease. So the physicians, according to the tumor's grade, can match the treatment of the brain tumor to the patient's individual needs and thus make the best course of treatment for each patient.

IDEFE algorithm: IDE algorithm optimizes the fuzzy entropy for the gland segmentation.

Breast cancer occurs in the epithelial tissue of the gland, so the accuracy of gland segmentation is crucial to the physician's diagnosis. An innovative technique for breast mammography image gland segmentation is put forth in this paper. In the first step, the algorithm designed the gland segmentation evaluation function. Then a new mutation strategy is established, and the adaptive controlled variables are used to balance the ability of improved differential evolution (IDE) in terms of investigation and convergence. To evaluate its performance, The proposed method is validated on a number of benchmark breast images, including four types of glands from the Quanzhou First Hospital, Fujian, China. Furthermore, the proposed algorithm is been systematically compared to five state-of-the-art algorithms. From the average MSSIM and boxplot, the evidence suggests that the mutation strategy may be effective in searching the topography of the segmented gland problem. The experiment results demonstrated that the proposed method has the best gland segmentation results compared to other algorithms.

Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.

Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables. The outcomes were evaluated with other works obtained from the literature to validate the suggested fuzzy systems for categorizing the Wisconsin breast cancer dataset. The fuzzy Inference Systems worked with different input features, according to the studies obtained from the literature. The outcomes confirm that most performance' metrics in several cases were greater than the achieved results from the literature for the output variable for the different Fuzzy Inference Systems-FIS, demonstrating superior precision.

Coupling ANFIS with ant colony optimization (ACO) algorithm for 1-, 2-, and 3-days ahead forecasting of daily streamflow, a case study in Poland.

Finding an efficient and reliable streamflow forecasting model has always been an important challenge for managers and planners of freshwater resources. The current study, based on an adaptive neuro-fuzzy inference system (ANFIS) model, was designed to predict the Warta river (Poland) streamflow for 1 day, 2 days, and 3 days ahead for a data set from the period of 1993-2013. The ANFIS was additionally combined with the ant colony optimization (ACO) algorithm and employed as a meta-heuristic ANFIS-ACO model, which is a novelty in streamflow prediction studies. The investigations showed that on a daily scale, precipitation had a very weak and insignificant effect on the river's flow variation, so it was not considered as a predictor input. The predictor inputs were selected by the autocorrelation function from among the daily streamflow time lags for all stations. The predictions were evaluated with the actual streamflow data, using such criteria as root mean square error (RMSE), normalized RMSE (NRMSE), and R2. According to the NRMSE values, which ranged between 0.016-0.006, 0.030-0.013, and 0.038-0.020 for the 1-day, 2-day, and 3-day lead times, respectively, all predictions were classified as excellent in terms of accuracy (prediction quality). The best RMSE value was 1.551 m3/s and the highest R2 value was equal to 0.998, forecast for 1-day lead time. The combination of ANFIS with the ACO algorithm enabled to significantly improve streamflow prediction. The use of this coupling can averagely increase the prediction accuracies of ANFIS by 12.1%, 12.91%, and 13.66%, for 1-day, 2-day, and 3-day lead times, respectively. The current satisfactory results suggest that the employed hybrid approach could be successfully applied for daily streamflow prediction in other catchment areas.

Adaptive fuzzy control of drug delivery in cancer treatment using combination of chemotherapy and antiangiogenic therapy.

This paper introduces the adaptive fuzzy control scheme as a promising control technique for cancer treatment from a theoretical point of view. A mathematical model describing the dynamics of tumor growth under the drug interventions of chemotherapy and antiangiogenic therapy is considered. The model incorporates the effects of normal cells, cancer cells, and endothelial cells. Then, the control goals in cancer treatment are discussed and the desired trajectory for a typical patient is derived using the optimal control theory. Since the dynamic model of tumor growth is not accurate and also varies from patient to patient, an adaptive fuzzy controller is designed to make the outputs of the dynamic model track the desired trajectory. The proposed control system is model-independent and identifies the dynamic model of tumor growth over time. The performance of the designed controller is assessed by several numerical simulations. Finally, a hardware-in-the-loop simulation is conducted to validate the results.

Aplicación de sistemas fuzzy para la toma de decisiones del Plan Paso a Paso: análisis de los indicadores para la comuna de Valdivia / Artificial intelligence fuzzy system for the design of confinements during quarantines

BACKGROUND: Different modalities of quarantines were one of the main measures implemented worldwide to avoid the spread of SARS-CoV2 virus. AIM: To analyze and compare retrospectively the implementation of the Step- to-Step plan devised by the Chilean Ministry of Health during the pandemic. To propose a decision-making path based on an artificial intelligence fuzzy system to determine confinements in specific territories. MATERIAL AND METHODS: The Step-to-Step Plan threshold values such hospital network capacity, epidemic spreading, testing and contact tracing capability were modeled using fuzzy numbers and fuzzy rule-based systems. RESULTS: Ministry of Health's decision-making opportuneness were unrelated with the Step-to-Step Plan indicators for deconfinement. Such disagreements undermined epidemiological indicators. CONCLUSIONS: Using an artificial intelligence system could improve decision-making transparency, emergency governance, and risk communication to the population.

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data.

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future.

An Intelligent System to Enhance the Performance of Brain Tumor Diagnosis from MR Images.

In the human body, cancer is caused by aberrant cell proliferation. Brain tumors are created when cells in the human brain proliferate out of control. Brain tumors consist of two types: benign and malignant. The aberrant parts of benign tumors, which contain dormant tumor cells, can be cured with the appropriate medication. On the other hand, malignant tumors are tumors that contain abnormal cells and an unorganized area of these abnormal cells that cannot be treated with medication. Therefore, surgery is required to remove these brain tumors. Brain cancers are manually identified and diagnosed by a skilled radiologist using traditional procedures. It's a lengthy and error-prone procedure. As a result, it is unsuitable for emerging countries with large populations. So computer-assisted automatic identification and diagnosis of brain tumors are recommended. This work proposes and implements a CAD system for the diagnosis of brain cancers using magnetic resonance imaging (MRI). Preprocessing, segmentation, feature extraction, and classification are the stages of automatic brain MRI processing that necessitate software based on a sophisticated algorithm. Image normalization with contourlet transform (INCT) is used in the preprocessing step to remove undesirable or noisy data. The performance metrics such as PSNR, MSE, and RMSE are computed. Then, the modified hierarchical k-means with firefly clustering (MHKFC) technique is used in the segmentation step to precisely recover the afflicted (tumor) area from the preprocessed image. The enhanced monarch butterfly optimization (EMBO) is used to select and then extract the most important gray-level co-occurrence matrix feature from the segmented image. The classification task was finally completed using the adaptive neuro-fuzzy inference system (ANFIS). The overall classification accuracy is 95.4% ( BRATS 2015), 96.6% ( BRATS 2021), and 93.7% (clinical data) is obtained.