Gray Level Co-occurrence Matrix Research Articles

Surface roughness analysis of side-polished fiber based on the importance of texture features

In order to explore the use of side-polished fibre (SPF) for microprobe-type "lab-on-fibre", this study presents an analysis of the surface roughness in side-polished fiber (SPF) using the gray level co-occurrence matrix (GLCM) texture feature analysis method. Experimental results show that the flat areas of the SPF polished surface exhibit texture characteristics with high mean values in contrast and entropy, and low mean values in the angular second moment (ASM), homogeneity, and correlation. Employing the random forest (RF) feature importance ranking method based on the Gini coefficient and out-of-bag (OOB) error estimation, this study assesses the sensitivity of various GLCM texture parameters in classifying different roughness levels of the SPF polished surfaces. A feature subset comprising variance, ASM, entropy, and contrast is identified as optimal. Utilizing this subset, the paper conducts an RF classification validation experiment on the roughness of the SPF polished surfaces, with results showing an RF classification accuracy of 95.65%.This research provides evidence for exploring the impact of rough polished surfaces in SPF optic sensors on the light coupling mechanism with environmental materials and its influence on sensor sensitivity. It lays the foundation for exploring the precise identification of high-sensitivity areas on SPF polished surfaces.

Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture.

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85-87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.

The Prediction and Evaluation of Surface Quality during the Milling of Blade-Root Grooves Based on a Long Short-Term Memory Network and Signal Fusion.

The surface quality of milled blade-root grooves in industrial turbine blades significantly influences their mechanical properties. The surface texture reveals the interaction between the tool and the workpiece during the machining process, which plays a key role in determining the surface quality. In addition, there is a significant correlation between acoustic vibration signals and surface texture features. However, current research on surface quality is still relatively limited, and most considers only a single signal. In this paper, 160 sets of industrial field data were collected by multiple sensors to study the surface quality of a blade-root groove. A surface texture feature prediction method based on acoustic vibration signal fusion is proposed to evaluate the surface quality. Fast Fourier transform (FFT) is used to process the signal, and the clean and smooth features are extracted by combining wavelet denoising and multivariate smoothing denoising. At the same time, based on the gray-level co-occurrence matrix, the surface texture image features of different angles of the blade-root groove are extracted to describe the texture features. The fused acoustic vibration signal features are input, and the texture features are output to establish a texture feature prediction model. After predicting the texture features, the surface quality is evaluated by setting a threshold value. The threshold is selected based on all sample data, and the final judgment accuracy is 90%.

Implementation of laser-light backscattering imaging for authentication of the geographic origin of Indonesia region citrus

Citrus fruit (Citrus nobilis Lour.) from the Indonesian region is reported to have high economic value due to attractive nutritional, nutraceutical, and sensory attributes. However, authenticating the geographic origins is challenging because of adulteration and similarity in visual appearance. Therefore, this study aimed to develop an effective method based on laser-light backscattering imaging (LLBI) for authentication of the geographic origin of the region citrus. A total of 200 citrus samples were collected from Medan, Malang, Jember, and Banyuwangi regions, which were the four main citrus-producing areas in Indonesia. Approximately three different laser wavelengths, namely 450, 532, and 648 nm were beamed to produce the backscattering image. Furthermore, a combination of the gray-level co-occurrence matrix (GLCM) method and support vector machine (SVM) algorithm was applied to extract texture features and build a classification model, respectively. In this context, three kernel functions, such as linear, radial basis function, and polynomial, were compared in authenticating the geographic origin of citrus. The results showed that the proposed technique achieved 96.667 % accuracy and 3.333 % apparent error for authentication of the geographic origin. The proposed LLBI technique applied a laser wavelength of 450 nm and a polynomial kernel function as the best combination to produce reliable predictive power. This study held valuable implications for advancing sensing technology devices to authenticate geographic origin, specifically citrus fruit.

Advanced skin disease detection: Image processing and modified genetic optimization with supervised k-nearest neighbors

The use of modern technology to improve skin disease diagnosis includes things like artificial intelligence (AL), deep learning (DL) and computer vision. Medical images of skin lesions, dermatoscopic images, or thermal imaging can be analyzed by automated algorithms and image processing techniques to diagnose skin diseases. Diseases can cause physical and emotional suffering, making them silent killers. Extreme instances might cause skin cancer. skin disease diagnosis from clinical photographs is a key medical image processing challenge. Manual skin diagnosis takes time and it is subjective for doctors. Computerized skin disease prediction can simplify patient and dermatologist treatment planning. To tackle these issues, we proposed a genetic optimization and supervised K-Nearest Neighbor (MGO-SKNN) technique for detecting advanced skin diseases. The research gathers clinical data for detecting advanced skin diseases. Three methods were used to preprocess our data: noise reduction, hair removal and image resizing. The Grey Level Co-Occurrence Matrix (GLCM) approach was used in this work to extract features. The integration of image processing into MGO-SKNN represents a significant step forward in the quest for accurate, efficient and cutting-edge skin disease diagnosis. The metrics like accuracy (97.53%), precision (95.20%), recall (96.74%) and f1-score (98.06%) of our suggested technique, MGO-SKNN, exceed the traditional approaches for skin detection.

Classification of Lung Disease in X-Ray Images Using Gray Level Co-Occurrence Matrix Method and Convolutional Neural Network

The lungs are a very important part of the human body, as they serve as a place for oxygen exchange. They have a very complex task and are susceptible to damage from the polluted air we breathe every day, which can lead to various diseases. Lung disease is a very common health problem that can be found in everyone, but there are still many people who do not pay attention to their lung health, making them vulnerable to lung disease. One of the methods used to detect lung disorders is by examining images obtained from X-rays. Image processing is one of the techniques that can also be used for lung disease identification and is most commonly used in medical images. Therefore, the purpose of this research is to implement image processing to determine the accuracy of lung disease identification using deep learning algorithms and the application of feature extraction. In this research, there are two experiments conducted consisting of the application of the classification method, namely Convolutional Neural Network and Gray Level Co-Occurrence Matrix feature extraction with CNN. The results show that the CNN model gets a precision of 0.92, recall of 0.92, f1-score of 0.92, and average accuracy of 0.92. The combination of the GLCM method with CNN produces a precision of 0.87, recall of 0.87, f1-score of 0.87, and average accuracy of 0.87. The results of this study indicate that the use of CNN in the lung disease classification model based on X-ray images is superior to the GLCM-CNN method.

Development and modification Sobel edge detection in tuberculosis X-ray images

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.

Extraction of collagen morphological features from second-harmonic generation microscopy images via GLCM and CT analyses: A cross-laboratory study.

Second-harmonic generation (SHG) microscopy provides a high-resolution label-free approach for noninvasively detecting collagen organization and its pathological alterations. Up to date, several imaging analysis algorithms for extracting collagen morphological features from SHG images-such as fiber size and length, order and anisotropy-have been developed. However, the dependence of extracted features on experimental setting represents a significant obstacle for translating the methodology in the clinical practice. We tackled this problem by acquiring SHG images of the same kind of collagenous sample in various laboratories using different experimental setups and imaging conditions. The acquired images were analyzed by commonly used algorithms, such as gray-level co-occurrence matrix or curvelet transform; the extracted morphological features were compared, finding that they strongly depend on some experimental parameters, whereas they are almost independent from others. We conclude with useful suggestions for comparing results obtained in different labs using different experimental setups and conditions.

Classification of Heart Sounds Using Grey Level Co-occurrence Matrix and Logistic Regression

Classification of Heart Sounds Using Grey Level Co-occurrence Matrix and Logistic Regression

A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information

BackgroundPepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images.ResultsThe results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%.ConclusionsThe research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

Enhancing cyber security: a comprehensive approach to the classification and prediction of significant cyber incidents (SCI) through data mining and variational neural network with fox optimization algorithm

The rapid advancement of technology and the proliferation of IoT devices have rendered cyberspace vulnerable, resulting in Significant Cyber Incidents (SCIs). This paper proposes an Enhancing Cyber security: a Comprehensive Approach to the Classification with Prediction of Significant Cyber Incidents through Data Mining with Variation Neural Network with Fox Optimization Algorithm (ECS-SCI-DM-VNN-FOA). The dataset is split into pre-pandemic and post-pandemic SCI subsets, according to the report from the Center for Strategic and International Studies (CSIS). Adaptive Variation Bayesian Filter (AVBF) is utilized to remove the noise from the input data. Then the preprocessed input data is supplied to the Improved Window Adaptive Gray Level Co-Occurrence Matrix (IWAGM) for feature extraction. The proposed approach is implemented in Python and its efficacy is evaluated under some metrics, like accuracy, precision, recall, FI-score, sensitivity, computational time, recall, and RoC. The proposed ECS-SCI-DM-VNN-FOA gives 24.91%, 23.76% and 25.98% high accuracy and 30.45%, 23.67% and 29.32% high precision compared with the existing methods, like classification with prediction of cyber events utilizing data mining with machine learning (CRP-SML), Cyber risk prediction via social media big data analytics along statistical machine learning (PECI-MUIP), Mining user interaction patterns in the dark web to forecast enterprise cyber occurrences (CTPA-CSCS) respectively.

MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma.

The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher's exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p = 0.045) and tumor volume (p = 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p ≤ 0.05). The regularized Cox regression analysis identified the "256 Angular Variance of Energy" texture feature as significantly associated with OS independently of the EC/MMMT grouping (p = 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes.

DCE-MRI Radiomic analysis in triple negative ductal invasive breast cancer. Comparison between BRCA and not BRCA mutated patients: Preliminary results

ObjectiveThe research aimed to determine whether and which radiomic features from breast dynamic contrast enhanced (DCE) MRI could predict the presence of BRCA1 mutation in patients with triple-negative breast cancer (TNBC). Material and methodsThis retrospective study included consecutive patients histologically diagnosed with TNBC who underwent breast DCE-MRI in 2010–2021. Baseline DCE-MRIs were retrospectively reviewed; percentage maps of wash-in and wash-out were computed and breast lesions were manually segmented, drawing a 5 mm-Region of Interest (ROI) inside the tumor and another 5 mm-ROI inside the contralateral healthy gland. Features for each map and each ROI were extracted with Pyradiomics-3D Slicer and considered first separately (tumor and contralateral gland) and then together. In each analysis the more important features for BRCA1 status classification were selected with Maximum Relevance Minimum Redundancy algorithm and used to fit four classifiers. ResultsThe population included 67 patients and 86 lesions (21 in BRCA1-mutated, 65 in non BRCA-carriers). The best classifiers for BRCA mutation were Support Vector Classifier and Logistic Regression in models fitted with both gland and tumor features, reaching an Area Under ROC Curve (AUC) of 0.80 (SD 0.21) and of 0.79 (SD 0.20), respectively. Three features were higher in BRCA1-mutated compared to non BRCA-mutated: Total Energy and Correlation from gray level cooccurrence matrix, both measured in contralateral gland in wash-out maps, and Root Mean Squared, selected from the wash-out map of the tumor. ConclusionsThis study showed the feasibility of a radiomic study with breast DCE-MRI and the potential of radiomics in predicting BRCA1 mutational status.

OPTIMIZED U-NET SEGMENTATION MODEL AND DEEP MAXOUT CLASSIFIER FOR BRAIN TUMOR CLASSIFICATION

The most serious nervous system ailment, a brain tumor impairs one’s health seriously and ultimately results in death. MRI, one of the most frequently used medical imaging modalities for brain tumors, has emerged as the main diagnostic system for the treatment and study of brain tumors. It was challenging to segment and classify the many kinds of brain tumors. The swarm intelligence approach has the potential to more efficiently and effectively tackle a number of issues. Therefore, this work develops a novel model for the classification of brain tumors that includes various phases. Primarily, the input image is preprocessed via the proposed median filtering that aids in removing noises. Subsequently, segmentation is done via optimal U-Net. For precise segmentation, the weights are tuned optimally by battle royale optimization with the Bernoulli randomization (BROBR) algorithm. Then, features like the proposed local Gabor XOR pattern (PLGXP), texton features, gray level co-occurrence matrix (GLCM), and correlation features are extracted. Finally, BTC is done using a deep maxout network (DMO) that provides the final output on the absence or presence of a brain tumor.

Full-Automatic High-Efficiency Mueller Matrix Microscopy Imaging for Tissue Microarray Inspection.

This paper proposes a full-automatic high-efficiency Mueller matrix microscopic imaging (MMMI) system based on the tissue microarray (TMA) for cancer inspection for the first time. By performing a polar decomposition on the sample's Mueller matrix (MM) obtained by a transmissive MMMI system we established, the linear phase retardance equivalent waveplate fast-axis azimuth and the linear phase retardance are obtained for distinguishing the cancerous tissues from the normal ones based on the differences in their polarization characteristics, where three analyses methods including statistical analysis, the gray-level co-occurrence matrix analysis (GLCM) and the Tamura image processing method (TIPM) are used. Previous MMMI medical diagnostics typically utilized discrete slices for inspection under a high-magnification objective (20×-50×) with a small field of view, while we use the TMA under a low-magnification objective (5×) with a large field of view. Experimental results indicate that MMMI based on TMA can effectively analyze the pathological variations in biological tissues, inspect cancerous cervical tissues, and thus contribute to the diagnosis of postoperative cancer biopsies. Such an inspection method, using a large number of samples within a TMA, is beneficial for obtaining consistent findings and good reproducibility.

Utilization of Texture Analysis in Differentiating Benign and Malignant Breast Masses: Comparison of Grayscale Ultrasound, Shear Wave Elastography, and Radiomic Features.

This study aims to determine which qualitative and quantitative US features are independently associated with malignancy, including those derived from grayscale imaging morphology, shear wave elastography (SWE), and texture analysis. This single-center retrospective study was approved by the institutional research ethics board. Consecutive breast US studies performed between January and December 2020 were included. Images were acquired using a Canon Aplio i800 US unit (Canon Medical Systems, Inc., CA) and i18LX5 wideband linear matrix transducer. Grayscale US features, SWE mean, and median elasticity were obtained. Single representative grayscale images were analyzed using dedicated software (LIFEx, version 6.30). First-order and gray-level co-occurrence matrix second-order texture features were extracted. Multivariate logistic regression was performed to assess for predictors of malignancy (STATA v16.1). One hundred forty-seven cases with complete SWE data were selected for analysis (mean age 54.3, range 21-92). The following variables were found to be independently associated with malignancy: age (P <.001), family history (P = .013), irregular mass shape (P = .024), and stiffness on SWE (mean SWE ≥40 kPa; P <.001). Remaining variables (including texture features) were not found to be independently associated with malignancy (P >.05). US texture analysis features were not associated with malignancy independent of other qualitative and quantitative US characteristics currently utilized in clinical practice. This suggests texture analysis may not be warranted when differentiating benign and malignant breast masses on US. In contrast, irregular mass shape on grayscale imaging and increased stiffness on SWE were found to be independent predictors of malignancy.

Ensemble-based deep learning model for welding defect detection and classification

This study introduces an ensemble-based deep learning approach for monitoring and detecting submerged arc weld defects in weld beads and adjacent zones during Non-Destructive Testing (NDT). The methodology utilizes an open-source image dataset to categorize submerged arc weld conditions into three defect types: cracks, lack of penetration and porosity, and ideal welds (no defects). To enhance image analysis, we employ preprocessing and feature extraction in both spatial and frequency domains using segmentation techniques like grey level difference method (GLDM), grey level co-occurrence matrix (GLCM), discrete wavelet transform (DWT), Fast Fourier Transform (FFT), and texture analysis. Following image preparation, the preprocessed data undergoes training and testing in our proposed ensemble-based deep learning model. The model's performance is thoroughly assessed using key metrics such as precision, recall, F1 score, receiver operating characteristics (ROC) curve, and a confusion matrix, resulting in an impressive accuracy rate of 93.12% for detecting and classifying weld faults. In comparing our model to state-of-the-art techniques in existing literature, our ensemble-based deep learning approach consistently demonstrates a high fault detection and classification performance in the face of a simplistic MLP-based core architecture, ratifying the robustness of our feature ensemble approach. The study concludes that this model has significant potential for integration into current inspection systems, offering an efficient and robust solution for real-time condition monitoring of welds along weldment joints.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Study on the EC prediction of cracked soda saline-alkali soil based on texture analysis of high-resolution images from ground-based observation and machine learning methods

The rapid and accurate measurement of soil salinity is essential for assessing the level of soil salinization. In natural conditions, the surface of cohesive soda saline-alkali soil experiences shrinkage and cracking phenomena during water evaporation. The propagation and development of these desiccation cracks can be effectively described using texture features due to their statistical randomness. This study aims to establish a relationship between electrical conductivity (EC) values and texture features derived from both gray-level co-occurrence matrix (GLCM) and wavelet decomposition, and to develop a prediction model for EC accordingly. To achieve these objectives, crack images on the surface of 200 soil samples with varying levels of salinity were obtained in the Songnen Plain field. GLCM was computed and wavelet decomposition was performed to extract texture features in different directions and scales. The results demonstrate a significant correlation between texture features and soil EC values. Among the various texture features, 12 GLCM texture features with a grayscale level of 2 and a step size of 1 pixel, and wavelet texture features derived from a 4th level orthogonal decomposition based on the coiflet-1 function were selected as the optimal parameters for establishing and comparing the predictive effects of two machine learning models on soil EC values. In comparison to the testing results of the BP neural network model (R2=0.83, RPD=1.64, RMSE=0.32 dS/m, MAE=0.18 dS/m), the random forest model exhibited higher accuracy and stability (R2=0.95, RPD=3.48, RMSE=0.21 dS/m, MAE=0.07 dS/m), indicating that although both machine learning models demonstrated rapid and nondestructive capabilities, the random forest method was better suited for EC prediction in soda saline-alkali soil due to its superior accuracy.

KLASIFIKASI HASIL MRI TUMOR OTAK DENGAN EKTRAKSI FITUR GRAY LEVEL CO-OCCURANCE MATRIX (GLCM)

An important part of the body is the brain which is the source of all the body's organs in the skull cavity. Brain tumors are one of the diseases that can attack it. Detection of brain tumors is one aspect that is considered important in medical diagnosis. This research aims to implement GLCM (Gray Level Co-occurrence Matrix) feature extraction on MRI images of brain tumors and to find the best algorithm performance for detecting brain tumors using these MRI images. The data used in this research is public data originating from kaggle.com. The feature extraction process in pictures used in this research is GLCM, which has the function of calculating the frequency of pixel intensity values ​​that are spaced between images using parameters 0o, 45o, 90o, and 135o. The next stage in this research is to carry out preprocessing steps and then look for classification values ​​from the MRI results using the Naïve Bayes, C4.5, and Neural Network algorithms. The results obtained show that Naïve Bayes has the best algorithm performance compared to C4.5 and Neural Network, namely with an accuracy of the Naïve Bayes algorithm of 96.8%, while for the C4.5 algorithm it is 41.5% and the Neural Network is 38.25%. Apart from this, this study proves that GLCM feature extraction has proven effective in capturing texture information from MRI images which is very important in brain tumor classification.