Gray Level Co-occurrence Matrix Features Research Articles

Machine learning and texture features based approach for classifying Alzheimer’s disease

In order to gain more insight into the nature of the disease and fill in numerous gaps in predictions and treatment, the problem of Alzheimer’s disease (AD) has been extensively studied. A lot of research has already been done to assist identify AD patients utilizing structural and textural features. In this work, texture features based machine learning models are proposed for multi-stage classification of Alzheimer’s disease. Multi-stage classification helps in identifying the disease at early stages and will reduce the suffering of the patients and their family members. The patients in this study are divided into four groups: CN (Cognitive Normal), AD (Alzheimer’s Disease), pMCI (Progressive Mild Cognitive Impairment), and sMCI (Static Mild Cognitive Impairment). The dataset is collected from the ADNI database and has about 15000 MRI raw images in ‘NIFTI’ format. In the first step, the data is preprocessed. Images are converted to the ‘jpg’ format after being stripped of their skulls. 8856 images are selected after preprocessing. For Feature Extraction, FOS (First-Order Statistical) and GLCM (Gray-Level Co-occurrence Matrix) features are utilized. Using normalization, the extracted features are scaled between 0 and 1. The normalized features are then used as input by SVM, Random Forest, Artificial Neural Network, and Decision Tree machine learning methods. Input features used for classification include GLCM, FOS features, and a combination of both. The performance is evaluated using accuracy, recall, F1-score, and precision parameters. The suggested method performs best when both GLCM and FOS features are provided as input. The most accurate method is Random Forest, which has a 66.1% accuracy rate. Decision tree, ANN, and SVM also performed well, with accuracies of 56.4, 58.5, and 59.2 respectively. The proposed approach has the potential for multi-stage classification of Alzheimer’s disease.

Automated Tomato Leaf Disease Detection Using Image Processing: An SVM‐Based Approach with GLCM and SIFT Features

Tomato cultivation is increasingly widespread, yet it faces significant challenges, particularly from plant diseases caused by fungi, bacteria, and insects. Addressing these diseases is crucial for ensuring the quality and yield of tomato crops. To support specialists in accurately identifying and managing these diseases, we propose an advanced automatic system for detecting and identifying tomato leaf diseases using sophisticated image processing techniques. Therefore, we proposed an approach that employs robust feature extraction methods, including the gray level co‐occurrence matrix (GLCM) and scale‐invariant feature transform (SIFT), coupled with a support vector machine (SVM) for adequate classification. We curated an extensive dataset of 2700 tomato leaf images, with a minimum of 300 images for each of the nine distinct disease classes. This comprehensive dataset facilitated the training and testing of various machine learning and deep learning models. The experimental results highlight our proposed approach’s exceptional accuracy and reliability, significantly improving the detection and classification of tomato leaf diseases. A thorough comparative analysis with contemporary state‐of‐the‐art techniques further validates the superiority of our system. Our findings suggest that this framework can significantly benefit tomato cultivation by enabling timely and precise disease management. Future research can explore integrating advanced deep learning algorithms to enhance the system’s accuracy in this multiclass classification challenge.

Enhancing the Efficacy of Radiomics-Based Prediction of Fuhrman Pathological Grading in Renal Clear Cell Carcinoma Using Multilayer Spiral CT Imaging.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal cell carcinoma (RCC). Conventional pathological methods of Fuhrman pathological grading system have limitations. This study aims to investigate the efficacy of radiomics-based multilayer spiral computed tomography (CT) imaging of Fuhrman pathological grading in ccRCC. A retrospective analysis was conducted on the clinical data of ccRCC patients admitted in our hospital from March 2023 to March 2024. The patients were classified as low-grade (Fuhrman pathological grades I and II) or high-grade (Fuhrman pathological grades III and IV). Statistical methods, including correlation analysis, receiver operating characteristic (ROC) curves and construction of a joint predictive model, were utilised to assess the predictive value of these imaging omics indicators for Fuhrman pathological grading in ccRCC. The primary outcome assessment parameter in this study was the predictive value of these imaging omics indicators for Fuhrman pathological grading in ccRCC. The clinical data from 101 ccRCC patients were examined, with 56 cases classified as low-grade and 45 cases as high-grade. The grey-level co-occurrence matrix (GLCM) features between low and high Fuhrman grading groups, including contrast (0.24 ± 0.08 vs. 0.33 ± 0.09), energy (0.73 ± 0.05 vs. 0.67 ± 0.06) and homogeneity (0.63 ± 0.05 vs. 0.57 ± 0.05), showed notable distinctions (p < 0.001). The CT imaging characteristics between low and high Fuhrman grading groups, including enhancement homogeneity (0.34 ± 0.08 vs. 0.26 ± 0.08) and washout half-time (28.57 ± 4.35 vs. 34.72 ± 5.62) demonstrated a substantial variation between the groups (p < 0.001). The enhancement homogeneity (r = 0.476), washout half-time (r = -0.519), contrast (r = 0.454), energy (r = -0.453) and homogeneity (r = -0.541) showed significant correlations with Fuhrman pathological grading. The predictive value of these features was evident, with a combined imaging genomics model exhibiting an area under the curve of 0.929. This study demonstrated the potential of radiomics-based prediction using multilayer spiral CT imaging for accurately predicting Fuhrman pathological grading in ccRCC.

Batik classification using KNN algorithm and GLCM features extraction

Batik is one of the Indonesian cultures that has been recognized by UNESCO as an intellectual right of Indonesia. The popularity of batik internationally raises concerns about the Indonesian people’s understanding of batik if Indonesian people only refer to all types of batik just as ‘batik’. By utilizing K-Nearest Neighbour (KNN) algorithm which is a simple classification algorithm, then a system can be created that can classify batik types. The first step of KNN is training, which stores each training pattern. The second step is classification, whenever classifying a pattern, KNN examine all training patterns to determine the K closest patterns using certain calculations such as Euclidean Distance and Manhattan Distance. Before classification, a characteristic that represents a pattern is needed. Gray-Level Co-Occurrence Matrix (GLCM) is an algorithm that has proven to be very powerful as a feature descriptor in representing the texture characteristic of an image. This research experiments with the value of K in KNN = 1, 3, 5, and 7 with the distance calculation using Euclidean and Manhattan. The GLCM characteristic used are Entropy, Energy, Contrast, Homogeneity, Dissimilarity, Correlation, ASM, and the average of each characteristic. From the research that has been done, the system created obtained the highest accuracy of 75% with the combination of parameters; pixel distance = 7, K value = 1, 1st fold as test data and 2nd and 3rd fold as training data, and by using StandardScaler. But despite getting decent accuracy, there is still a need for further research to improve the accuracy of batik classification.

Razvoj modela mašinskog učenja zasnovanog na algoritmu slučajnih šuma za detekciju promena u tkivu jetre nakon izlaganja česticama oksida gvožđa

Introduction/Aim: The aim of our study was to create a machine learning model, specifically a random forest model, which uses textural data from liver micrographs to differentiate between normal hepatic tissue and damaged tissue exposed to iron oxide nanoparticles. Material and Methods: Regions of interest in micrographs of hepatic tissue, obtained from mice treated with iron oxide nanoparticles and controls, were analyzed using the gray-level co-occurrence matrix (GLCM) method. The resulting GLCM features were employed as input data for the training and testing of the random forest model using the "Scikit-learn" library in the Python programming language. Additionally, a conventional decision tree model was developed, based on the classification and regression tree (CART) algorithm. Results: The random forest model outperformed the alternative CART decision tree approach in terms of classification accuracy, correctly predicting the class for 73.67% of the instances in the validation ROI dataset. The area under the receiver operating characteristic curve was 0.81, indicating relatively good discriminatory power. The F1 score for the model was 0.74, showcasing fairly good precision and recall, though not perfect. Conclusion: The data obtained from this study may be utilized for further development of artificial intelligence computation systems to identify physiological and pathophysiological changes in hepatic tissue. The results also serve as a starting point for additional research on the automation of histopathological analysis of liver tissue exposed to external toxic agents.

Custom Convolution Neural Network for Breast Cancer Detection

Breast cancer remains a serious global health issue. Leveraging the use of deep learning techniques, this study presents a custom Convolutional Neural Network (CNN) framework for the detection of breast cancer. With the specific objective of accurate classification of breast cancer, a framework is made to analyze high-dimensional medical image information. The CNN's architecture, which consists of specifically developed layers and activation components tailored for the categorization of breast cancer, is described in detail. Utilizing the BreakHis dataset, which comprises biopsy slide images of patients in a range of cancer stages, the model is trained and verified. Comparing our findings to conventional techniques, we find notable gains in sensitivity, specificity, and accuracy. Gray-Level Co-Occurrence Matrix (GLCM) features extracted from the BreakHis dataset was used to analyze the performance on sequential neural network, transfer learning and machine learning models. After analysis, we have proposed hybrid models of CNN-SVM, CNN-KNN, CNN-Logistic regression and achieved accuracy of about 95.2%.

Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification.

An automated detection and classification of brain tumor from MRIs using Water Chaotic Fruitfly Optimization (WChFO) based Deep Recurrent Neural Network (DRNN)

AbstractDue to the complexity of the images and dearth of anatomical models, it is highly difficult to accurately represent the various deformations in each component of the medical images. In recent years, a significant number of children and adults have affected from brain tumors, which is one of the most terrible types of disease affects the people around the world. Moreover, the Magnetic Resonance Imaging (MRI) based brain tumor detection is one of a significant study area in the field of medical imaging. Since, the use of computerized methods aids in the detection and treatment of disease by the medical professionals. The development of an automated method for the accurate detection and classification of tumors from brain MRIs. In this framework, a tanh normalization process is used to smooth out the input brain MRIs with less noise artefacts and improved quality. Then, a group feature extraction model is used to extract the relevant features from the normalized image, which includes both Speeded Up Robust Features (SURF) and Grey Level Co-occurrence Matrix (GLCM) features. The Water Chaotic Fruitfly Optimization (WChFO) method is used to identify the best features for increasing the speed of classifier training and testing processes with less time. Moreover, a Deep Recurrent Neural Network (DRNN) model is used to classify the type of brain tumor for accurate early diagnosis and treatment. The most well-known benchmarking datasets, like BRATS and Kaggle, employed for analysis in order to assess the effectiveness and results of the proposed brain tumor diagnosis system. By using the proposed WChFO-DRNN technique, the accuracy of the tumor detection system is increased to 99.2% with the sensitivity, specificity of 99% and time consumption of 0.2s.

A Cost-Effective Vital Sign Monitoring System Harnessing Smartwatch for Home Care Patients

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to relatively small cell sizes and overlapping cell nuclei. Therefore, an accurate analysis of the Pap smear image is essential to obtain the right information. This research compares nucleus segmentation and detection using gray-level cooccurrence matrix (GLCM) features in two methods: Otsu and polynomial. The data tested consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate characteristics. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for future studies in developing new methods to enhance identification accuracy.

An automated method for tendon image segmentation on ultrasound using grey-level co-occurrence matrix features and hidden Gaussian Markov random fields

Background:Despite knowledge of qualitative changes that occur on ultrasound in tendinopathy, there is currently no objective and reliable means to quantify the severity or prognosis of tendinopathy on ultrasound. Objective:The primary objective of this study is to produce a quantitative and automated means of inferring potential structural changes in tendinopathy by developing and implementing an algorithm which performs a texture based segmentation of tendon ultrasound (US) images. Method:A model-based segmentation approach is used which combines Gaussian mixture models, Markov random field theory and grey-level co-occurrence (GLCM) features. The algorithm is trained and tested on 49 longitudinal B-mode ultrasound images of the Achilles tendons which are labelled as tendinopathic (24) or healthy (25). Hyperparameters are tuned, using a training set of 25 images, to optimise a decision tree based classification of the images from texture class proportions. We segment and classify the remaining test images using the decision tree. Results:Our approach successfully detects a difference in the texture profiles of tendinopathic and healthy tendons, with 22/24 of the test images accurately classified based on a simple texture proportion cut-off threshold. Results for the tendinopathic images are also collated to gain insight into the topology of structural changes that occur with tendinopathy. It is evident that distinct textures, which are predominantly present in tendinopathic tendons, appear most commonly near the transverse boundary of the tendon, though there was a large variability among diseased tendons. Conclusion:The GLCM based segmentation of tendons under ultrasound resulted in distinct segmentations between healthy and tendinopathic tendons and provides a potential tool to objectively quantify damage in tendinopathy.

A computer aided system for skin cancer detection based on Developed version of the Archimedes Optimization algorithm

In recent years, skin cancer has been recognized as the most dangerous and common type of cancer in humans. Melanoma is a common skin cancer, and the diagnosis of melanoma in the early stages of the disease can significantly prevent death from this deadly skin cancer. Providing a method that facilitates the diagnosis of melanoma in the early stages is very useful and valuable. The current paper proposes a new methodology for the best diagnosis of melanoma cancer using dermoscopic images. The proposed method begins with a normalization of scaling the data to a standard range and a histogram equalization to enhance the quality of the input images. Then, some different features based on the Gray-Level Co-occurrence Matrix (GLCM) are extracted from the image. GLCM features capture the spatial distribution and correlation of the pixel intensities, which reflect the texture information of the images. For reducing the complexity of the method, minimum features have been selected using a newly Developed version of Archimedes Optimization Algorithm (DAOA). Then, the selected features are classified by a Support Vector Machine (SVM) to distinguish between benign and malignant lesions. The proposed method is applied to the American Cancer Society (ACS) dataset, which consists of 68 pairs of TLM and XLM images with a size of 180×180 pixels. The results have been compared with five different methods based on five performance indicators: precision, sensitivity, accuracy, specificity, and F-measure. The results indicate that the presented approach gives a proper efficiency for the diagnosis of the melanoma. The proposed method achieves the highest values for all the performance indicators among the compared methods. The proposed method achieves an accuracy of 88 %, a sensitivity of 96 %, a specificity of 81 %, a precision of 97 %, and an F-measure of 97 % for the diagnosis of melanoma.

Classification of Palm Fruit Ripeness Level Using Learning Vector Quantization (LVQ) Method

The implementation of Learning Vector Quantization (LVQ) to classify the ripeness of oil palm fruits is investigated in this study. In addition, this study provides a comprehensive set of procedures ranging from data collection and pre-processing to training and testing of the LVQ model, and finally, the proposed method has been validated by testing it on previously unseen data. Three feature extraction methods, specifically Gray-Level Co-occurrence Matrix (GLCM), Hue, Saturation, and Value (HSV), and t-Distributed Stochastic Neighbor Embedding (t-SNE), were assessed for their performance. The results show that the chosen feature extraction method strongly influences the classification performance. The accuracy of the model employing t-SNE features is notably the highest at 50%, indicating its efficacy in identifying the ripeness level of palm fruits by extracting pertinent features. On the other hand, the GLCM feature has a 40% accuracy in the test data, suggesting that although it captures information on texture, it may not comprehensively encapsulate ripeness characteristics. Additionally, the HSV feature achieves an accuracy of 45%, which is less precise than that of t-SNE. To conclude, this study elucidates the appropriateness of various feature extraction techniques in categorizing the degree of ripeness in palm fruits. The t-SNE feature extraction model stands out as the most efficient option, exhibiting greater precision in comparison to other methodologies.

A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis.

Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention. This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel-wise consistency for deep learning-based MRI-to-CT synthesis. The method was expected to assist the multi-modality studies for radiomics. The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre-procession. We proposed a gray-level co-occurrence matrix (GLCM)-based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning-based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function. Compared with the baseline, the proposed method improved the pixel-wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78±0.12 to 0.82±0.11 (p<0.05 for paired t-test). Generally, >90% (22/24) of the GLCM-based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs. The proposed method reproduced texture features for MRI-to-CT synthesis, which would benefit radiomics studies based on image multi-modality synthesis.

A novel automated feature selection based approach to recognize cauliflower disease

Cauliflower disease is a primary cause of reduced cauliflower yield. Preventing cauliflower disease requires early diagnosis. In the scope of this study, we suggested an agro-medical expert system that would make it easier to diagnose cauliflower disease. In this method, a digital image must be taken off the phone or handled device to diagnose cauliflower sickness. A data augmentation technique was initially used to construct a vast data set. The disease-affected parts of the cauliflower were then segmented using k-means clustering. Following that, ten statistical and gray-level co-occurrence matrix (GLCM) features were retrieved from the segmented pictures. After choosing the top n features (N ranged from 5 to 10), the synthetic minority oversampling technique (SMOTE) approach was used to handle training datasets with different amounts of each feature. After that, we utilized five machine learning (ML) algorithms and evaluated their performance using seven performance evaluation matrices for both augmented and non-augmented datasets. The same procedure was performed on both datasets. Then, we use both datasets to test how well the classifier works. Logistic regression (LR) is the most accurate method for the top nine features in the augmented dataset (90.77%).

Evaluating the effects of texture features on Pinus sylvestris classification using high-resolution aerial imagery

Pinus sylvestris, also known as Scots pine, is the most widespread type of pine tree found around the world. Texture features can recognize patterns in an image's texture and so distinguish different species with similar spectral properties. They also record information about the spatial layout of pixels inside an image. Nonetheless, previous research has not addressed the effects of textural variables on Scots pine classification using high-resolution aerial images. In the context of this investigation, we used TreeSatAI, a benchmark dataset with categorized high-resolution aerial imagery, to conduct a comparative analysis, and we examined a comprehensive set of 93 texture features (radiomics) by generating several parameters such as First Order, Gray Level Co-occurrence Matrix (GLCM), Gray Level Dependence Matrix (GLDM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM). We proceeded to train machine learning models utilizing the state-of-the-art algorithms such as XGBoost, LightGBM, and Gradient Boosting Machines (GBM). Upon conducting a more comprehensive analysis, it has come to light that the GLDM and GLRLM features have exhibited greater significance in the realm of satellite image processing research, specifically pertaining to the precise classification of Scots pine. This finding challenges the conventional reliance on GLCM features, which have traditionally been favored in similar studies. This study is remarkable due to its emphasis on the efficacy of employing high-resolution aerial imagery and texture features within the realm of satellite image processing applications.

Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner.

18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.

Machine learning in the detection of dental cyst, tumor, and abscess lesions

Background and ObjectiveDental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions.Materials & MethodsThe current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis.ResultsThe findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM.ConclusionThe GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.

Novel MobileNet based Multipath Convolutional Neural Network for defect detection in fabrics

Automatic fabric defect detection and classification is the most important process in the textile industry to ensure the fabric quality. In the existing systems, a learning based method is used for detecting defects in plain weave fabrics. In this paper, a novel MobileNet based Multipath Convolutional Neural Network (MMPCNN) architecture is proposed for detection and classification of simple and complex patterned fabric defects. In the proposed MMPCNN architecture, MobileNet model is used in the first path. In this, Gabor filter bank is used instead of conventional filters in the first convolution layer. A simple convolutional neural network architecture with Gray Level Co-occurrence Matrix (GLCM) features as an input is used in the second path of the MMPCNN architecture. Gabor filters are more useful for analyzing the texture with different orientations and scales. Each Gabor filter parameter has its own impact on analyzing the texture and extracting the information from the texture. Therefore, in this paper, the use of Gabor filter parameters in MMPCNN architecture is analyzed. The proposed model is experimented on the TILDA textile image database and it is able to achieve 100% accuracy with reduced trainable parameters for fabric defect detection and classification.

Pneumonia Classification Based on GLCM Features Extraction using K-Nearest Neighbor

&lt;p class="Abstract"&gt;Pneumonia has been detected using Machine learning. The stages in this study began with preprocessing in 4 stages: resizing, cropping, filtering using a high pass filter, and Adaptive Histogram Equalization. The feature extraction process continued with 22 Gray Level Co-occurrence Matrix (GLCM) features and classification using K-Nearest Neighbor (KNN). The image used was 150 data sets for training on the classification of 3 classes with a ratio of 50:50:50 while training on two classes was 50 bacterial pneumonia and 50 viral pneumonia. The most optimal training data accuracy results were obtained using the angle direction on the GLCM, namely 135o with the KNN classification (k = 3). For the classification of two classes Using 40 data sets, an accuracy of 91% was obtained, while testing for three classes with 60 data sets was 83.3%.&lt;/p&gt;

SVM for Classification of Brain MRI with K-Means for Detection

The segmentation of MR brain images using this method is based on K-means clustering, feature extraction using a discrete wavelet transform (DWT), and feature selection using a grey level co-occurrence matrix (GLCM). For this procedure, we have used a Perfect Radial Basis Function (RBF) - Support Vector Machine (SVM) Classifier. Based on fractions of selectivity and sensitivity, the classifier's performance was measured in terms of accuracy. The proposed classifier was found to be 93% accurate. Additionally, the Histogram methodology was applied in this proposed method in place of randomly choosing the cluster centres. Keywords: K-means clustering, Histon creation, Discrete Wavelet Transform (DWT), GLCM feature selection, and RBF- SVM Classifier.