Fuzzy C-means Technique Research Articles

Integrating Fuzzy C-Means Clustering and Explainable AI for Robust Galaxy Classification

The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing a degree of uncertainty. The objective of this study is to integrate Fuzzy C-Means (FCM) clustering with explainability methods to achieve a precise and interpretable model for galaxy classification. We applied FCM to manage this uncertainty and group galaxies based on their morphological characteristics. Additionally, we used explainability methods, specifically SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-Agnostic Explanations), to interpret and explain the key factors influencing the classification. The results show that using FCM allows for accurate classification while managing data uncertainty, with high precision values that meet the expectations of the study. Additionally, SHAP values and LIME provide a clear understanding of the most influential features in each cluster. This method enhances our classification and understanding of galaxies and is extendable to environmental studies on Earth, offering tools for environmental management and protection. The presented methodology highlights the importance of integrating FCM and XAI techniques to address complex problems with uncertain data.

Development of an unsupervised pseudo-deep approach for brain tumor detection in magnetic resonance images

Brain tumor detection has long been a significant and challenging issue in medical research. In this study, we propose an unsupervised pseudo-deep method for detecting brain tumors in Magnetic Resonance (MR) images. Our approach utilizes iterative spectral Co-Clustering and Fuzzy C-means techniques to achieve precise segmentation results. In each iteration, the algorithm recognizes the tumor block from its input image using spectral Co-Clustering. The selected block from the previous iteration serves as the input for the subsequent iteration. In the final iteration, Fuzzy C-Means is applied to the last selected block to extract the tumor, and a scheme is proposed to determine the location of the tumor in the original image. Through this iterative pseudo-deep method, our approach exhibits a layered-like structure, resembling deep learning architectures, within the context of unsupervised methods. We evaluated our approach using the BraTS2020 and BraTS2021 datasets and assessed its performance using key metrics. Our method achieved values of 99.12% and 81.42% for accuracy and dice coefficient on BraTS2020 and also 99.21% and 82.03% on the BraTS2021 dataset, surpassing the performance of existing unsupervised methods reported in the literature. Moreover, our approach demonstrates notably superior performance when dealing with complex images. The proposed method offers a unique perspective in the application of unsupervised techniques for brain tumor detection using a pseudo-deep structure.

Detection of Megakaryocyte Cell Structure Through Artificial Intelligence Tools

Recent research has focused on analysing megakaryocyte images to extract the information needed to track the progression of nervous system diseases. Segmentation is a fundamental step in describing and analysing the core contents of megakaryocytes, including the cytoplasm and nucleus. In this study, 45 megakaryocyte images were obtained. A new segmentation image technique was proposed, called the updating fuzzy c-means technique, through the intelligent selection of the centres of each cluster to separate cell components. The first step of this technique (fuzzification) was based on a knowledge analysis of the local parameters (entropy, contrast and standard deviation) that had a substantial influence on the grey-level distribution between the cytoplasm and nucleus. The second important step was the construction of fuzzy rules in terms of the variation in these local parameters to control the intelligent pick-out or update the centroid of each cluster and obtain a successful separation of the cytoplasm and nucleus. The final step was defuzzification to obtain the output images. The results revealed the superiority of the proposed method over recent technique. The accuracy of the segmented nucleus was greater than 7.46%; in the case of the cytoplasm, the accuracy was higher at 18%. These results indicated that this technique may be applied on other biomedical images.

Hyperspectral crop image classification via ensemble of classification model with optimal training

Agriculture is a significant source of income, and categorizing the crop has turned into vital factor that aids more in the crop production sector. Traditionally, crop development stage determination is done manually by eye inspection. However, producing high-quality crop type maps using modern approaches remains difficult. In this paper, the hyperspectral crop image classification model is proposed that includes four stages, they are (a) preprocessing, (b) segmentation, (c) feature extraction and (d) classification. In the preprocessing step, the hyperspectral image is provided as input, where the filtering process will carried out using median filtering. The filtered image is then used as the segmentation’s input. The image is segmented in the segmentation step using the enhanced entropy-based fuzzy c-means technique. Subsequently, spectral spatial features and vegetation index-based features are derived from segmented images. The final step is the classification, where the ensemble of classification model will be used that includes models like Convolutional Neural Networks (CNN), Deep Maxout (DMO), Recurrent Neural Networks (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU), respectively. The proposed Self Improved Tasmanian devil Optimization (SI-TDO) approach has optimally adjusted the Bi-GRU model’s training weights to enhance ensemble classification performance. Finally, the effectiveness of the proposed SI-TDO method compared to the traditional algorithm is examined for several metrics. The SI-TDO obtained the greatest accuracy of 94.68% in training rate 80, while other existing models have the lowest ratings.

A semi-supervised approach of short text topic modeling using embedded fuzzy clustering for Twitter hashtag recommendation

Social media stands as a crucial information source across various real-world challenges. Platforms like Twitter, extensively used by news outlets for real-time updates, categorize news via hashtags. These hashtags act as pivotal meta-information for linking tweets to underlying themes, yet many tweets lack them, posing challenges in topic searches. Our contribution addresses this by introducing a novel heuristic for hashtag recommendation. Extracting 20 thousand tweets, 5000 each from distinct categories health, sports, politics, and technology we applied fundamental data cleaning and tokenization techniques. Leveraging Word2Vec, we vectorized tokens, capturing nuanced semantic meanings and mitigating data sparsity issues. The proposed heuristic creates clusters of different topic by combining these embedded features and idea of fuzzy C-Means technique. Develop a rule-based approach that combines both supervised and unsupervised methods to label clusters, indicating their respective topic. The experimental outcomes shows that our proposed techniques achieve better performance metrics in precision, recall, and F1-score compared to specific baseline models.

Skin Cancer Segmentation On Dermoscopy Images Using Fuzzy C-Means Algorithm

Millions of people around the world suffer from skin cancer, a common and sometimes fatal disease. Dermoscopy has become an effective diagnostic technique for skin cancer. Precise segmentation is essential for skin cancer diagnosis. Segmentation allows more precise analysis of dermoscopic images by defining the boundaries of the lesion and separating it from surrounding healthy tissue. Dermoscopy images served as a source of research data, and Fuzzy C-Means (FCM) segmentation techniques were used. FCM is a promising method and has received a lot of attention lately. FCM is able to distinguish the various components within the lesion and effectively separate the lesion from the surrounding area. As a result, the distribution of membership degree values of each pixel in the image for each cluster represents the segmentation results obtained through FCM. The FCM technique for segmenting dermoscopic images is expected to significantly improve the precision and effectiveness of skin cancer diagnosis.

Smart system for identifying the various pathologies in MR brain image using Monkey Search based Interval Type-II Fuzzy C-Means technique

Smart system for identifying the various pathologies in MR brain image using Monkey Search based Interval Type-II Fuzzy C-Means technique

Handling incomplete data using Radial basis Kernelized Intuitionistic Fuzzy C-Means

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy Ⅽ-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy Ⅽ-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques.

Proposing new clustering-based algorithms for the multi-skilled resource-constrained multi-project scheduling problem with resource leveling adjustments

PurposeThe target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.Design/methodology/approachThe K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.FindingsComparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.Practical implicationsThe practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.Originality/valueReviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.

An entropy-based membership approach on type-II fuzzy set (EMT2FCM) for biomedical image segmentation

Medical image segmentation is a challenging task owing to its aliasing artifacts, existence of mixed pixels, interference of noise and blurring effect, etc. The brain and the spinal cord together constitute the Central Nervous System (CNS), with the brain being the most complex component. Brain segmentation is crucial for surgical planning, disease detection, and therapeutic interventions. A unique entropy based soft clustering approach (EMT2FCM) for the brain MRI imaging on both real and simulated datasets is proposed in this paper. This paper presents a refined version of the type-2 Fuzzy C-Means (FCM) technique (EMT2FCM) for isolating diverse tissues in Magnetic Resonance (MR) brain images, achieved through an enhanced entropy-based membership function. Difficulty in edge detection occurs due to the vagueness caused by mixed pixels. To overcome the problem with mixed pixels a new membership function proposal has been given. EMT2FCM, i.e., fuzzy membership function with Shannon's entropy-based variation, is able to handle uncertainty and vagueness in images offered by mixed pixels, by implementing new distance criteria modification for the calculation of optimum cluster. The approach has been validated using datasets from BRAINWEB (simulated) and IBSR (real-life). Following the deployment of our new algorithm, we assessed its performance using six observation criteria. The results demonstrated significant improvements in clustering performance, as evidenced by enhanced Mean Squared Error (MSE), Dice Index, and Jaccard Index, Accuracy, Sensitivity and Partition Indices validation measures. The evaluation of EMT2FCM in comparison to other state-of-the-art methods is established to reduce segmentation errors significantly.

Micro-segmentation of retinal image lesions in diabetic retinopathy using energy-based fuzzy C-Means clustering (EFM-FCM).

Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World Health Organization (WHO). The condition is characterized by pathological abnormalities in the retinal layer, including microaneurysms, vitreous hemorrhages, and exudates. Microscopic analysis of retinal images is crucial in diagnosing and treating DR. This article proposes a novel method for early DR screening using segmentation and unsupervised learning techniques. The approach integrates a neural network energy-based model into the Fuzzy C-Means (FCM) algorithm to enhance convergence criteria, aiming to improve the accuracy and efficiency of automated DR screening tools. The evaluation of results includes the primary dataset from the Shiva Netralaya Centre, IDRiD, and DIARETDB1. The performance of the proposed method is compared against FCM, EFCM, FLICM, and M-FLICM techniques, utilizing metrics such as accuracy in noiseless and noisy conditions and average execution time. The results showcase auspicious performance on both primary and secondary datasets, achieving accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. The proposed method holds significant potential in medical image analysis and could pave the way for future advancements in automated DR diagnosis and management. RESEARCH HIGHLIGHTS: A novel approach is proposed in the article, integrating a neural network energy-based model into the FCM algorithm to enhance the convergence criteria and the accuracy of automated DR screening tools. By leveraging the microscopic characteristics of retinal images, the proposed method significantly improves the accuracy of lesion segmentation, facilitating early detection and monitoring of DR. The evaluation of the method's performance includes primary datasets from reputable sources such as the Shiva Netralaya Centre, IDRiD, and DIARETDB1, demonstrating its effectiveness in comparison to other techniques (FCM, EFCM, FLICM, and M-FLICM) in terms of accuracy in both noiseless and noisy conditions. It achieves impressive accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s.

Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms

Objective: The aim of the study is to predict the absorbed radiation dose on thorax CT imaging in geriatric patients with COVID-19. Materials and Method: The SIEMENS SENSATION 64 CT scanner was performed with real protocols to patients (male/female phantom) using Monte Carlo simulation methods with the patient’s real height and weight nts and the actual parameters CT scanner. Absorbed organ doses have been calculated based on these Monte Carlo results. These results were used to predict the optimal absorbed radiation dose by Artificial Neural Network, Linear Discriminant Analysis, Random Forest Classification, and Naive-Bayes Classification algorithms. The dose values were clustered for genders by the Fuzzy C-Means algorithm. Results: The ages of the patients were between 60 and 70 years. The Body Mass Indexes of male and female patients were 26.11 ± 4.49 and 25.03 ± 4.86 kg/m2 respectively. All classification algorithms were validated with approximately 100% success. The Fuzzy C-Means technique was found to be successful in clustering the dose values for gender clusters. Conclusion: While the predicted and the observed values of patients do not change in the organs/tissues around and outside of the thorax, they generally vary in the intra-thoracic organs and tissues. It can be concluded that data-driven techniques are useful to obtain optimal radiation doses for organs/tissues in CT imaging.

Antimicrobial study and biosorption of Pb2+ ions onto chitosan-walnut composites: mechanistic studies and neuro-fuzzy modeling approach

The upsurge in the discharge of lead ions (Pb2+) into the environs resulting from various anthropogenic activities vis-vis its adverse effect on public health is a call for great concern. However, the adsorption technique, amongst other heavy metal removal methods, is regarded as the most promising. The present study synthesized a walnut shell-chitosan composite (WNS-CH) as an efficient biosorbent for Pb2+ uptake and biofilter of Bacillus subtilis and Klebsiella pneumoniae. WNS-CH was characterized using SEM and FTIR. Furthermore, an intelligent and cost-effective machine learning model, an adaptive neuro-fuzzy model clustered with the grid-partitioning (GP), and fuzzy c-means (FCM) technique were developed to predict the adsorption of Pb2+ based on relevant input parameters. The batch adsorption was carried out by varying operating parameters such as contact time, temperature, pH, adsorbent dose, and initial adsorbate concentration. The SEM images of WNS-CH showed a homogenous regular hollow ellipsoidal morphologies, while FTIR spectra showed the presence of O-H, N-H, C-N, and C-O. Under the conditions of initial pH 10, dosage 45 mg, and temperature of 40 °C, an adsorption efficiency of 94 % was obtained. The thermodynamic parameters, ∆H° and ∆G°, showed an endothermic and spontaneous process for Pb2+ uptake. Antibacterial activities of the WNS-CH composite showed bioactivity against Bacillus subtilis and Klebsiella pneumoniae with a mean ZI of 5.3±1.16 and 6.0 ±1.00, respectively. The experimental data was best described by Freundlich isotherm (R2= 0.9509) and pseudo-first-order kinetic (R2= 0.9674) models indicating chemisorption and multilayer adsorption process. The best prediction of Pb2+ adsorption was obtained with the optimal GP-clustered ANFIS model using a triangular membership function (triMF), giving Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and correlation determination (R1) values of 1.217, 0.563, 1.698, and 0.9966 respectively at the testing phase. The GP-ANFIS model shows good agreement with experimental results. This study revealed that WNS-CH composite could be regarded as a promising biosorbent for the remediation of Pb2+-polluted wastewater. The cost analysis demonstrated that the WNS-CH composite could serve as an alternative to commercial activated carbon.

Conjunction of hard k-mean and fuzzy c-mean techniques in clustering and identifying some critical meteorological parameters for thunderstorm formation over a metro city of India during pre-monsoon season

Conjunction of hard k-mean and fuzzy c-mean techniques in clustering and identifying some critical meteorological parameters for thunderstorm formation over a metro city of India during pre-monsoon season

A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification.

In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.

A Clustering Approach Based on Fuzzy C-Means in Wireless Sensor Networks for IoT Applications

Sensor nodes in Wireless Sensor Network (WSN)-based Internet of Things (IoT) networks are often battery-powered, resulting in supplying relatively low energy. Energy efficiency in WSN-based IoT systems is a critical challenge as the IoT becomes more sophisticated owing to its widespread adoption. Clustering-based routing approaches are well-known approaches that have distinct benefits in terms of efficient communication, scalability, and network lifespan extension. In this research, we present a novel clustering technique for WSN-based IoT systems based on Fuzzy C-Means (FCM). To pick the best Cluster Head (CH), the method uses an FCM technique to build the clusters and a reduction in the total energy spent on each cluster. Rather than replacing CHs for dynamic clustering at each period in this study, we plan to use an energy threshold to hypothesize the dynamicity of CH dependent on existing energy levels, therefore increasing the sensor network lifespan

Performance Evaluation of the Impact of Clustering Methods and Parameters on Adaptive Neuro-Fuzzy Inference System Models for Electricity Consumption Prediction during COVID-19

Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens’ quality of life and ensure an organization’s seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive clustering (SC), fuzzy c-means (FCM), and other key parameters such as clustering radius (CR), input and output membership functions, and the number of clusters. Using renowned statistical metrics, the best sub-models for each clustering technique were selected. The outcome showed that the ANFIS-based FCM technique produced the best results with five clusters, with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Variation (RCoV), Coefficient of Variation of the Root Mean Square Error (CVRMSE), and Mean Absolute Percentage Error (MAPE) being 1137.6024, 898.5070, 0.0586, 11.5727, and 9.3122, respectively. The FCM clustering technique is recommended for usage in ANFIS models that employ similar time series data due to its accuracy and speed.

A Cognitive Systems Engineering Approach Using Unsupervised Fuzzy C-Means Technique, Exploratory Factor Analysis and Network Analysis-A Preliminary Statistical Investigation of the Bean Counter Profiling Scale Robustness.

A bean counter is defined as an accountant or economist who makes financial decisions for a company or government, especially someone who wants to severely limit the amount of money spent. The rise of the bean counter in both public and private companies has motivated us to develop a Bean Counter Profiling Scale in order to further depict this personality typology in real organizational contexts. Since there are no scales to measure such traits in personnel, we have followed the methodological steps for elaborating the scale’s items from the available qualitative literature and further employed a cognitive systems engineering approach based on statistical architecture, employing cluster, factor and items network analysis to statistically depict the best mathematical design of the scale. The statistical architecture will further employ a hierarchical clustering analysis using the unsupervised fuzzy c-means technique, an exploratory factor analysis and items network analysis technique. The network analysis which employs the use of networks and graph theory is used to depict relations among items and to analyze the structures that emerge from the recurrence of these relations. During this preliminary investigation, all statistical techniques employed yielded a six-element structural architecture of the 68 items of the Bean Counter Profiling Scale. This research represents one of the first scale validation studies employing the fuzzy c-means technique along with a factor analysis comparative design.

Identification of homogenous rainfall regions with trend analysis using fuzzy logic and clustering approach coupled with advanced trend analysis techniques in Mumbai city

Climatic conditions are significantly changing in the urban areas in the age of climate change. However, due the dynamic nature of urban areas and uneven distribution of meteorological stations, it is difficult to study rainfall pattern in the urban areas. Hence, in this study, trend and pattern of rainfall have been examined of Mumbai city using rainfall regionalization approach. For this, K-means and fuzzy C-means (FCM) clustering approaches have been used to examine the pattern of rainfall. The innovative trend analysis (ITA) and five tests of Mann-Kendall (MK) family have been utilized for anaylzing the rainfall trend. Result shows that both K-means and FCM techniques have identified two homogeneous rainfall regions in Mumbai exhibiting two distinctive rainfall patterns. The ITA curve shows that in the lower cluster, the data points are below 1:1 (45o) line, but in high and moderate cluster, they are above the line. This shows that there is a positive trend in the rainfall, but it is not monotonic. Further, the MK tests utilized in this study also shows positive trend in the rainfall in Mumbai. The result of this study may help in management of water resource and in urban flood mitigation in Mumbai city.

Comparison of Human Intestinal Parasite Ova Segmentation Using Machine Learning and Deep Learning Techniques

Helminthiasis disease is one of the most serious health problems in the world and frequently occurs in children, especially in unhygienic conditions. The manual diagnosis method is time consuming and challenging, especially when there are a large number of samples. An automated system is acknowledged as a quick and easy technique to assess helminth sample images by offering direct visibility on the computer monitor without the requirement for examination under a microscope. Thus, this paper aims to compare the human intestinal parasite ova segmentation performance between machine learning segmentation and deep learning segmentation. Four types of helminth ova are tested, which are Ascaris Lumbricoides Ova (ALO), Enterobious Vermicularis Ova (EVO), Hookworm Ova (HWO), and Trichuris Trichiura Ova (TTO). In this paper, fuzzy c-Mean (FCM) segmentation technique is used in machine learning segmentation, while convolutional neural network (CNN) segmentation technique is used for deep learning. The performance of segmentation algorithms based on FCM and CNN segmentation techniques is investigated and compared to select the best segmentation procedure for helminth ova detection. The results reveal that the accuracy obtained for each helminth species is in the range of 97% to 100% for both techniques. However, IoU analysis showed that CNN based on ResNet technique performed better than FCM for ALO, EVO, and TTO with values of 75.80%, 55.48%, and 77.06%, respectively. Therefore, segmentation through deep learning is more suitable for segmenting the human intestinal parasite ova.