A comparison of platelets classification from digitalization microscopic peripheral blood smear

In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM)

The face is one of the media to identify someone, a human face has a very high level of variability. Many methods have been introduced by researchers and scientists in recognizing one's face, one of the methods introduced is the Feature Extraction of Gray Level Co-Occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ). GLCM feature extraction is used for data extraction/learning process whereas a data analysis process (face recognition, cropping and storing data) the LVQ method is used for the data training process where the data that has been processed in GLCM feature extraction which still has large dimensions are processed to be smaller dimensions. So this test uses data of 190 photos and gets a match of 90%, the authors conclude that the GLCM feature extraction and LVQ method can very well recognize faces contained in the database.

Platelet and Red Blood Cell Indices in Harris Platelet Syndrome.

The quality of fruit production is very important because it is related to the value of sales. Data from the Directorate General of Horticulture at the Ministry of Agriculture in 2017 showed that 94,3% of the total yield of citrus fruits is a type of tangerine. In the classification of the quality, the visual observation process is strongly influenced by subjectivity so that in certain conditions such as tired eyes and the number of oranges that want to classify too many the process can be inconsistent and also take a long time. Therefore, a technology is needed to accelerate the classification process and make it more objective. This study combines the Gray level Co-occurrence Matrix (GLCM) method for texture, Hue, Saturation, Value (HSV) features for color features and the k-Nearest Neighbor (k-NN) classification method. The data used were 60 images of rotten tangerines and 60 images of not rotten tangerines divided using a 4-fold cross-validation method to find the best combination of data training and data testing. 3 main processes will be carried out, namely preprocessing, feature extraction and classification. This study produced the highest accuracy of 80% from the combined of GLCM and HSV features extraction with value k = 5 for k-NN .

The gray level co-occurrence matrix (GLCM) algorithm is a contemporary computational biology method which, today, is frequently used to detect small changes in texture that are not visible using conventional techniques. We demonstrate that the toxic compound 6-hydroxydopamine (6-OHDA) and iron oxide nanoparticles (IONPS) have opposite effects on GLCM features of cell nuclei. Saccharomyces cerevisiae yeast cells were treated with 6-OHDA and IONPs, and imaging with GLCM analysis was performed at three different time points: 30 min, 60 min, and 120 min after the treatment. A total of 200 cell nuclei were analyzed, and for each nucleus, 5 GLCM parameters were calculated: Angular second moment (ASM), Inverse difference moment (IDM), Contrast (CON), Correlation (COR) and Sum Variance (SVAR). Exposure to IONPs was associated with the increase of ASM and IDM while the values of SVAR and COR were reduced. Treatment with 6-OHDA was associated with the increase of SVAR and CON, while the values of nuclear ASM and IDM were reduced. This is the first study to indicate that IONPs and 6-OHDA have opposite effects on nuclear texture. Also, to the best of our knowledge, this is the first study to apply the GLCM algorithm in Saccharomyces cerevisiae yeast cells in this experimental setting.

Parasitised red blood cells misclassified as giant platelets by an automated digital morphology analyser (Sysmex DI-60/CellaVision): a case report and a retrospective EQA analysis.

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach

Melanoma is one of the rare and malignant types of skin cancer. There are numerous ways to detect the melanoma, and one of them is through doctor diagnosis. A doctor can detect melanoma after a thorough medical check-up. If the patient has symptoms of melanoma, a biopsy will be carried out. This process requires a long time making it inconvenient. Therefore, an approach of the image processing system is necessary to assist the experts in diagnosing melanoma. The process consists of image input using the dermoscopy image, a pre-processing process of grey-scaling and median filtering, and feature extraction using Grey-Level Co-Occurrence Matrix (GLCM). In the final step, a classification process will be performed using learning vector quantization. Based on the experimental test, the system generated an accuracy of 83.33% in identifying melanoma cancer.

Research on the Identification of Stroke with MRI Imagery Using the Learning Vector Quantization (LVQ) Method Based on Texture Features has been carried out. This study aims to determine the program's best parameters and the highest accuracy level of the stroke identification program. This research was conducted at Haji Sukolilo Hospital - Surabaya by obtaining 57 images of stroke patients and 15 images of regular patients. The study used the intelligence of stroke, tumor, and standard images to determine each category's image characteristics. After knowing the differences in each class, the next process is digital image processing, followed by feature extraction used is the Gray-Level Co-occurrence matrix (GLCM) with four parameters: contrast, correlation, diversity, and homogeneity. These four parameters are the best input parameters with an intelligence rate of 0.100 with a decrease in intelligence rate of 0.100, so the best accuracy value for training is 74.97%, and test data is 78.60%. Regarding the program's ability to correctly identify 11 data from 14 data tested, the program is feasible to be used as a second opinion.

This study aims to develop an AI-based diagnostic tool for classifying dental conditions and tooth types to enhance the accuracy and efficiency of dental diagnostics. Manual documentation and diagnosis in dentistry are often prone to errors, inefficiencies, and delays, leading to adverse patient outcomes. Leveraging digital image processing and machine learning, this research addresses these challenges by automating the classification process. Dental imaging data were collected from the Dental and Mouth Hospital (RSGM) of Nala Husada Surabaya, Indonesia, comprising 3,910 images categorized into dental conditions (1,767 images) and tooth types (2,143 images). The dataset was preprocessed through resizing, grayscale conversion, histogram equalization, and median filtering. Texture features were extracted using the Gray Level Co-occurrence Matrix (GLCM), and classification was performed using Support Vector Machine (SVM), K-Nearest Neighbor, Naïve Bayes, Decision Tree, and Random Forest algorithms. The SVM algorithm achieved the highest accuracy of 54.24% for dental conditions and 41.49% for tooth types, outperforming other methods. However, the overall performance was suboptimal, primarily due to dataset limitations, reliance on GLCM for feature extraction, and insufficient preprocessing. The results highlight the potential of AI-based tools in dentistry but also underscore the need for improvements in dataset diversity, advanced feature extraction methods, and hyperparameter optimization. Future research should focus on expanding the dataset, exploring deep learning-based feature extraction, and employing robust evaluation strategies to enhance model performance. This study lays the groundwork for developing a more reliable and efficient AI-based diagnostic tool, ultimately improving patient outcomes and streamlining clinical workflows in dentistry.

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of $$88.5\%$$ , HOG with Fine-KNN achieves $$86.5\%$$ accuracy and LBP with LDA achieves the accuracy of $$97.9\%$$ .

Defective platelet responsiveness to thrombin and protease-activated receptors agonists in a novel case of gray platelet syndrome: correlation between the platelet defect and the alpha-granule content in the patient and four relatives.

Diversity on durian varieties makes it difficult to distinguish between durian varieties. In addition, some durian varieties also have similarities which further complicate the classification process, and are known as typical tropical fruits, these fruits are native to Southeast Asia and have been introduced throughout the world. The ability to recognize objects is needed by humans. Recognition can facilitate human daily activities. Pattern recognition is often applied to a variety of objects, one of which is pattern recognition on fruit. There is some limitations of human memory in remembering object features. Based on this problem, research was conducted to identify fruit plants based on its features using digital image processing techniques, Learning Vector Quantization (LVQ) algorithm and Gray Level Co-occurrence Matrix (GLCM). Each type of plant has different shapes, colors, and textures; this is what makes the durian unique to other durians. The program was created using Microsoft Visual C # 2010 software. The test results achieved an 89% success rate in recognizing fruit plants based on the fruit.

Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named general training, distillation training and autoencoder training to optimize and improve the model accuracy and inference performance. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 99.23% while requiring just over 4600 floating point operations. For practical validation of model efficiency, we have deployed the miniaturized model in different mobile phones and a server-backed web application. Data gathered from these environments show that the model can be used to perform inference under 1 s per sample in both offline (mobile only) and online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.

In clinical practice it is very important to perform the platelet counts. The estimation of platelet counts from peripheral blood smears is an accurate method and provides adequate quality assurance in traditional methods as the automated cell counters are not available at all hospital setups especially in rural areas. In modern era use of automated analyzers based on impedance technology has resulted in improvement of accuracy and helps in measurement of platelet indices such as Mean Platelet Volume (MPV) Plateletcrit (PCT) and Platelet Distribution Width (PDW). However, both the methods have certain limitations. Platelet counts were estimated on a 5-part Differential Automated Hematology Analyzer and manually on Leishman stained peripheral blood SMEAR.
 Aims & Objective: 1. This study was conducted to compare platelet counts by peripheral blood smear method and automated method in patients attending OPD on routine basis in a mountainous hilly region of J&k .
 
 It also aims to study the relation, if any, between the platelet count (automated) and platelet indices like Mean Platelet Volume (MPV) and Platelet Distribution Width (PDW) and to assess the possible role of these parameters in certain defined situations.
 
 Materials and methods: A prospective study was conducted in a tertiary care hospital on 200 Ethylene Diamine Tetra Acetic acid (EDTA) anticoagulated blood samples. Samples were evaluated by 5-Part differential automated hematology analyser using impedance counting method and by examination of peripheral blood smear method (PBF).
 Results: In thrombocytopenic patients, the platelet count assessed by automated analyzer revealed an inverse relation with Mean Platelet Volume (MPV) and Platelet Distribution Width (PDW) and showed large granular and giant platelets on PBS.
 Conclusion: Automated hematology analyzer is essential for immediate and accurate complete blood count evaluation but blood samples showing erroneous results or low platelet counts on analyzer should be confirmed on peripheral blood smear. The platelet indices like Mean Platelet Volume (MPV) and Platelet Distribution Width (PDW) can point to the underlying pathology especially in cases of thrombocytopenia.
 Keywords: Automated analyser, Peripheral blood smear, MPV, PDW, large granular platelets.