Gray Level Run Length Research Articles

Deciphering 18F-DOPA uptake in SDH-related head and neck paragangliomas: a radiomics approach.

To investigate the influence of germline succinate dehydrogenase (SDHx) pathogenic variants on 6-[18F]-fluoro-3,4-dihydroxyphenylalanine (18F-DOPA) Positron Emission Tomography (PET) radiomic signature of head and neck paragangliomas (HNPGLs). Forty-seven patients (20 SDH pathogenic variants carriers) harboring 55 HNPGLs were retrospectively included. HNPGLs were delineated using Nestle adaptive threshold. 128 radiomic features were extracted and harmonized to correct for batch effects. Principal Component Analysis (PCA) was performed to remove redundancy and avoid collinearity. The most representative feature of each component was tested with multivariate stepwise logistic binary regression analysis (LBRA) to identify variables predictive of genetic status. 18F-DOPA Positron Emission Tomography/Computed Tomography (PET/CT) detected 28/29 carotid body HNPGLs, 23/23 jugulotympanic HNPGLs, and 4/4 vagal HNPGLs. SUVmax was significantly higher in SDH-related HNPGLs (p = 0.003). PCA allowed identification of 4 Components. The most representative variables of Component 1 and 2 (including intensity and intensity-related textural features, and not intensity-related textural features, respectively) were Intensity-based (IB)-SUVmedian and Grey Level Run Length Matrix-Long Run Low Gray Level Emphasis (GLRLM-LRLGLE). SDHx HNPGLs exhibited higher activity scores and more homogeneous texture. At patient level, SDHx cases showed significantly higher IB-SUVmedian values (p < 0.001), and lower GLRLM-LRLGLE than sporadic patients (p = 0.005). IB-SUVmedian was found to be an independent predictor of genetic status at lesion (71.0%) and patient level (77.8%). The present study pioneers the application of 18F-DOPA PET radiomics for HNPGLs, suggesting the influence of germline SDH pathogenic variants on 18F-FDOPA uptake intensity and textural heterogeneity. Integrating radiomics with genetic data provides new insights into the correlation between PET features and underlying molecular dysregulation.

Predicting Tumor Progression in Patients with Cervical Cancer Using Computer Tomography Radiomic Features

The objective of this study was to evaluate the effectiveness of utilizing radiomic features from radiation planning computed tomography (CT) scans in predicting tumor progression among patients with cervical cancers. A retrospective analysis was conducted on individuals who underwent radiotherapy for cervical cancer between 2015 and 2020, utilizing an institutional database. Radiomic features, encompassing first-order statistical, morphological, Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Dependence Matrix (GLDM) features, were extracted from the primary cervical tumor on the CT scans. The study encompassed 112 CT scans from patients with varying stages of cervical cancer ((FIGO Staging of Cervical Cancer 2018): 24% at stage I, 47% at stage II, 21% at stage III, and 10% at stage IV). Of these, 31% (n = 35/112) exhibited tumor progression. Univariate feature analysis identified three morphological features that displayed statistically significant differences (p &lt; 0.05) between patients with and without progression. Combining these features enabled a classification model to be developed with a mean sensitivity, specificity, accuracy, and AUC of 76.1% (CI 1.5%), 70.4% (CI 4.1%), 73.6% (CI 2.1%), and 0.794 (CI 0.029), respectively, employing nested ten-fold cross-validation. This research highlights the potential of CT radiomic models in predicting post-radiotherapy tumor progression, offering a promising approach for tailoring personalized treatment decisions in cervical cancer.

Prostate Cancer Classification and Interpretation With Multiparametric Magnetic Resonance Imaging and Gleason Grade Score Using DarkNet53 Model.

Prostate Cancer (PCa) increases the mortality rate of males worldwide and is caused by genetics, lifestyle, and age reasons. The existing automated PCa classification systems face difficulties with overfitting issues, and non-generalizability, leading to poor classification performance. On this account, this study proposes an automated classification of PCa from MRI images using a hybrid weighted mean of vectors-optimized DarkNet53 classifier model. The proposed method suggests nonlocal mean filtering for noise reduction, N4ITK bias field correction to enhance image quality, and active contour-based segmentation for accurately identifying the disease region. The feature extraction utilizes the gray level run length matrix and shape features for effective feature extraction. A weighted mean of vectors optimization is used to optimize the feature selection process by hybridizing it with the DarkNet53 model for classification. Finally, the interpretation of achieving the classification has been demonstrated using the explainable AI Grad-CAM model. After comparing the proposed work with various state-of-the-art algorithms, the proposed model achieves 99.31% accuracy, 98.24% sensitivity, and 98.46% specificity, respectively, highlighting the model's accomplishment using the DarkNet53 classifier.

A Composite Meta Model for the Identification of Cotton Pathologies Utilizing an IoT-Enabled Framework and Stacked Generalization Learning Methodology

This research proposes a novel framework for predicting cotton plant diseases using IoT, deep learning, and meta-heuristic optimization techniques. High-definition images of cotton leaves are captured in the field, processed through IoT, and enhanced using a Probabilistic Hybrid Wiener Filter. The Modified Dilated U-Net segments pathological regions, while features are extracted using Improved Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRLM). Feature dimensionality is reduced by the Binary Guided Whale-Dipper Throated Optimizer. The classification uses an ensemble of deep learning models—EfficientNet-B7, ResNet50, VGG19, DenseNet121, and InceptionV3—optimized by Harris whale optimization to determine weight coefficients. The system accurately detects diseases like Army Worms, Powdery Mildew, and Bacterial Blight with 99.66% accuracy. This IoT-enabled framework provides efficient real-time disease detection, benefiting cotton farmers and the textile industry. A field study was conducted in the summer (Kharif) season of 2022–23 in North Maharashtra region to assess cotton cultivation utilizing IoT sensor data analyzed within the ThingSpeak IoT framework. The proposed methodology, leveraging a dataset of the images of cotton leaves demonstrate a remarkable precision rate of 99.66%. The amalgamation of IoT sensor data with deep learning methodologies enables the early prompt identification of diseases in cotton plant leaves. The suggested ensemble framework demonstrates enhanced efficacy in comparison to alternative models.

Classification of Liver Lesion Stages using pyRadiomics Features Combined with 3D-CNN in 3D-CT and US Images

Objectives: The goal of this study is to identify the various stages in liver diseases employing pyRadiomics features by analyzing the 3-dimensional CT and US images. Methods: The study uses Gray level Run Length Matrix for feature extraction and 3D-CNN for classification and compared with methods like Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) classifiers to assess the efficiency of the proposed model. The suggested algorithm is implemented using the library functions in Tensorflow and Keras packages in Python. Findings: The study utilizes radiomic feature extraction and two-phase classification to detect and classify liver disorders. In the first phase, binary classification is used to classify US and CT images as normal or abnormal, with accuracy rates of 95.4% and 97.9%. This is followed by the second phase classification, which classifies the lesion stages for both US and CT aberrant images. CT images have been shown to be more accurate in categorizing lesion stages than US imaging. CT images have a much lower misclassification rate at each step than US images. Finally, the effectiveness of the 3D-CNN model is compared with SVM, RF, and NB and the suggested method outperforms the other methods. Novelty: The suggested architecture employs radionics feature extraction with two-phase CNN for classification and the suggested method is found to be more efficient than SVM, RF, and NB. Keywords: Radiomics; Feature Extraction; Convolutional Neural Networks (CNN); Classification; Liver Diseases

Radiomics for Predicting Prognostic Factors in Breast Cancer: Insights from Contrast-Enhanced Mammography (CEM).

Objectives: To evaluate the correlation between radiomic features extracted from contrast-enhanced mammography (CEM) tumor lesions and peritumoral background with prognostic factors in breast cancer (BC). Methods: In this retrospective, single-center study, 134 women with histologically confirmed breast cancer underwent CEM examination. Radiomic features were extracted from manually segmented lesions and lesion contours were automatically delineated using PyRadiomics. The extracted features were categorized into seven classes: First-order Features, Shape Features (2D), Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM). Histological examination assessed tumor type, grade, receptor structure (ER, PgR, HER2), Ki67 index, and lymph node involvement. Pearson correlation and multivariate regression were applied to evaluate associations between radiomic features and prognostic factors. Results: Significant correlations were found between First-order Features and prognostic factors such as ER, PgR, and Ki67 (p < 0.05). GLCM-based texture features showed strong associations with Ki67 and HER2 (p < 0.01). Radiomic features from peritumoral regions, especially shape and GLSZM metrics, were significantly correlated with Ki67 and lymph node involvement. Conclusions: Radiomic analysis of both tumor and peritumoral regions offers significant insights into BC prognosis. These findings support the integration of radiomics into personalized diagnostic and therapeutic strategies, potentially improving clinical decision making in BC management.

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.

Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non-invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches. Radiomics analyzes images objectively by extracting quantitative features and can potentially reveal biological characteristics that the human eye cannot detect. However, the reproducibility and repeatability of some radiomic features can be highly susceptible to segmentation and imaging conditions. We aimed to assess the reproducibility of radiomic features extracted from uncorrected MPI-SPECT images reconstructed with 15 different settings before and after ComBat harmonization, along with evaluating the effectiveness of ComBat in realigning feature distributions. A total of 200 patients (50% normal and 50% abnormal) including rest and stress (without attenuation and scatter corrections) MPI-SPECT images were included. Images were reconstructed using 15 combinations of filter cut-off frequencies, filter orders, filter types, reconstruction algorithms, number of iterations and subsets resulting in 6000 images. Image segmentation was performed on the left ventricle in the first reconstruction for each patient and applied to 14 others. A total of 93 radiomic features were extracted from the segmented area, and ComBat was used to harmonize them. The intraclass correlation coefficient (ICC) and overall concordance correlation coefficient (OCCC) tests were performed before and after ComBat to examine the impact of each parameter on feature robustness and to assess harmonization efficiency. The ANOVA and the Kruskal-Wallis tests were performed to evaluate the effectiveness of ComBat in correcting feature distributions. In addition, the Student's t-test, Wilcoxon rank-sum, and signed-rank tests were implemented to assess the significance level of the impacts made by each parameter of different batches and patient groups (normal vs. abnormal) on radiomic features. Before applying ComBat, the majority of features (ICC: 82, OCCC: 61) achieved high reproducibility (ICC/OCCC ≥ 0.900) under every batch except Reconstruction. The largest and smallest number of poor features (ICC/OCCC<0.500) were obtained by IterationSubset and Order batches, respectively. The most reliable features were from the first-order (FO) and gray-level co-occurrence matrix (GLCM) families. Following harmonization, the minimum number of robust features increased (ICC: 84, OCCC: 78). Applying ComBat showed that Order and Reconstruction were the least and the most responsive batches, respectively. The most robust families, in a descending order, were found to be FO, neighborhood gray-tone difference matrix (NGTDM), GLCM, gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and gray-level dependence matrix (GLDM) under Cut-off, Filter, and Order batches. The Wilcoxon rank-sum test showed that the number of robust features significantly differed under most batches in the Normal and Abnormal groups. The majority of radiomic features show high levels of robustness across different OSEM reconstruction parameters in uncorrected MPI-SPECT. ComBat is effective in realigning feature distributions and enhancing radiomic features reproducibility.

Combination of FDG PET/CT radiomics and clinical parameters for outcome prediction in patients with non-Hodgkin's lymphoma.

The purposes was to build model incorporating PET + computed tomography (CT) radiomics features from baseline PET/CT + clinical parameters to predict outcomes in patients with non-Hodgkin lymphomas. Cohort of 138 patients with complete clinical parameters and follow up times of 25.3 months recorded. Textural analysis of PET and manual correlating contouring in CT images analyzed using LIFE X software. Defined outcomes were overall survival (OS), disease free-survival, radiotherapy, and unfavorable response (defined as disease progression) assessed by end of therapy PET/CT or contrast CT. Univariable and multivariable analysis performed to assess association between PET, CT, and clinical. Male ( P = 0.030), abnormal lymphocytes ( P = 0.030), lower value of PET entropy ( P = 0.030), higher value of SHAPE sphericity ( P = 0.002) were significantly associated with worse OS. Advanced stage (III or IV, P = 0.013), abnormal lymphocytes ( P = 0.032), higher value of CT gray-level run length matrix (GLRLM) LRLGE mean ( P = 0.010), higher value of PET gray-level co-occurrence matrix energy angular second moment ( P < 0.001), and neighborhood gray-level different matrix (NGLDM) busyness mean ( P < 0.001) were significant predictors of shorter DFS. Abnormal lymphocyte ( P = 0.033), lower value of CT NGLDM coarseness ( P = 0.082), and higher value of PET GLRLM gray-level nonuniformity zone mean ( P = 0.040) were significant predictors of unfavorable response to chemotherapy. Area under the curve for the three models (clinical alone, clinical + PET parameters, and clinical + PET + CT parameters) were 0.626, 0.716, and 0.759, respectively.

Pavement Distress Detection Based on the Staged Deep Learning Classifier Approach

Pavement crack detection is a critical task that is essential to ensure the safety of roads and highways. Accurate detection and classification of pavement cracks can help transportation agencies to identify potential maintenance needs and plan appropriate interventions. In this project, we propose a staged deep learning classifier approach for pavement crack detection to provide accurate and comprehensive information about the pavement condition. In this proposed pavement crack detection model, there are four main phases: pre-processing, feature extraction, feature selection, and crack detection. Firstly, raw images are pre-processed through median filtering and histogram equalization for noise removal and contrast enhancement. Next, features such as the improved local binary pattern, gray-level co-occurrence matrix, and gray-level run length matrix are extracted from the pre-processed images. The optimal features are selected using a hybrid optimization model, the tuna customized honey badger optimization algorithm, which combines the honey badger algorithm and tuna swarm optimization. This new hybrid optimization model will enhance the search strategy for solving optimization problems. The loss function of an augmented convolutional neural network (A-CNN) is modified to compute the root mean square error instead of the entropy-based loss function. The final detected outcomes (presence or absence of cracks) are acquired from the A-CNN.

Radiomics-Based Risk Assessment Correlates With Outcomes in Patients With Acute Type B Aortic Dissection Undergoing Thoracic Endovascular Repair.

Our study aimed to investigate the correlations between radiomics-based assessment and outcomes, including positive aortic remodeling (PAR), reintervention for dissection at 1 year, and overall survival, in patients with Type B aortic dissection (TBAD) who underwent thoracic endovascular aortic repair (TEVAR). This was a single-center, retrospective, cohort study. The cohort comprised 104 patients who had undergone TEVAR of TBAD in our institution between January 2010 and October 2022. We segmented preoperative computed tomography (CT) images of the patients' descending aorta regions, then extracted a comprehensive set of radiomic features, including first-order features, shape features (2D and 3D), gray-level co-occurrence matrix (GLCM), gray-level size zone matrix, gray-level run length matrix, gray-level dependence matrix, neighborhood gray-tone difference matrix, from the regions of interest. Next, we selected radiomics features associated with total descending aorta positive aortic remodeling (TDA-PAR) and reintervention by least absolute shrinkage and selection operator (LASSO) regression and features associated with survival by LASSO-Cox regression. This enabled us to calculate radiomics-based risk scores for each patient. We then allocated the patients to high and low radiomics-based risk groups, the cutoff being the median score. We used 3 different models to validate the radiomics-based risk scores. The patients' baseline characteristics did not differ between those who achieved TDA-PAR and those who did not. The radiomics-based risk scores were significantly and independently associated with all 3 outcomes. As to the impact of specific radiomics features, we found that GLSZM_SmallAreaLowGrayLevelEmphasis and shape_Maximum2DDiameterColumn had positive impacts on both reintervention and survival outcomes, whereas GLCM_Idmn positively affected survival but negatively affected reintervention. We found that radiomics-based risk for TDA-PAR correlated most significantly with zone 6 PAR. Radiomics-based risk scores were significantly associated with the outcomes of TDA-PAR, reintervention, and overall survival. Radiomics has the potential to make significant contributions to prediction of outcomes in patients with TBAD undergoing TEVAR. In this study of 104 patients with Type B aortic dissection, we demonstrated associations between radiomics-based risk and postoperative outcomes, including total descending aorta positive aortic remodeling, reintervention and survival. These findings highlight radiomics' potential as a tool for risk stratification and prognostication in acute Type B aortic dissection management.

Quality Assessment of Rice Using Convolution Neural Networks and Other Machine Learning Techniques- A Comparative Study

Rice holds its position as the most widely cultivated crop worldwide, its demand steadily rising alongside population growth. The precise identification of grains, especially wheat and rice, holds significant importance as their ultimate utilization depends on the quality of grains prior to processing. Traditionally, grain identification tasks have been predominantly manual, relying on experienced grain inspectors and consuming considerable time. However, this manual classification process is susceptible to variations influenced by individual perception, given the subjective nature of human image interpretation. Consequently, there is an urgent need for an automated recognition system capable of accurately identifying grains under diverse environmental conditions, necessitating the application of digital image processing techniques. In this study, we focus on grading five distinct varieties of rice based on their quality, employing a range of convolution neural networks (CNN) namely, Efficientnetb0, Googlenet, MobileNetV2, Resnet50, Resnet101, and ShuffleNet. The performance of CNN towards identification and grading of rice grain is also compared to that of other parametric and Non-parametric classifiers namely, Linear Discriminant Analysis (LDA), K-Nearest Neighbor (K-NN), Naive Bayes (NB), and Back Propagation Neural Network (BPNN) using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) based texture features. The image dataset comprises five grades of rice, each containing 100 images, resulting in a comprehensive collection of 500 samples for analysis. It is observed that, Convolution neural networks can grade five different qualities of rice with highest accuracy of 64.4% in case of GoogleNet. Results show that, rice grading using texture features performs better with highest accuracy of 99.2% (using GLCM) and 93.4% (using GLRLM).

CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) remains a leading cause of cancer deaths. Despite the rise of immunotherapies, many HCC patients don't benefit. There's a clear need for biomarkers to guide treatment decisions. This research aims to identify such biomarkers by combining radiological data and machine learning. We analyzed clinical and CT imaging data of 54 HCC patients undergoing immunotherapy. Radiologic features were examined to develop a model predicting short-term immunotherapy effects. We utilized 9 machine learning and 2 ensemble learning techniques using RapidMiner for model construction. We conducted the validation of the above feature combination using 29 HCC patients who received immunotherapy from another hospital, and tested and validated it using XGBoost combined with a genetic algorithm. In 54 HCC patients, radiomics features varied significantly between those with partial response (PR) and stable disease (SD). Key features in Gray Level Run Length Matrix (GLRLM) and in adjacent tissues' Intensity Direct, Neighborhood Gray Tone Difference Matrix (NGTDM), and Shape correlated with short-term immunotherapy efficacy. Selected feature combinations of 15, 19, and 8/15 features yielded better outcomes. Deep learning, random forest, and naive bayes outperformed other methods, with Bagging being more reliable than Adaboost. In the validation set of 29 HCC patients, the mentioned feature combination also demonstrated favorable performance. Furthermore, we achieved improved results when employing XGBoost in conjunction with a genetic algorithm for testing and validation. The machine learning model built with CT image features derived from GLCM, GLRLM, IntensityDirect, NGTDM, and Shape can accurately forecast the short-term efficacy of immunotherapy for HCC.

An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images

As the quantity and significance of digital pictures in the medical industry continue to increase, Image Quality Assessment (IQA) has recently become a prevalent subject in the research community. Due to the wide range of distortions that Magnetic Resonance Images (MRI) can experience and the wide variety of information they contain, No-Reference Image Quality Assessment (NR-IQA) has always been a challenging study issue. In an attempt to address this issue, a novel hybrid Artificial Intelligence (AI) is proposed to analyze NR-IQ in massive MRI data. First, the features from the denoised MRI images are extracted using the gray level run length matrix (GLRLM) and EfficientNet B7 algorithm. Next, the Multi-Objective Reptile Search Algorithm (MRSA) was proposed for optimal feature vector selection. Then, the Self-evolving Deep Belief Fuzzy Neural network (SDBFN) algorithm was proposed for the effective NR-IQ analysis. The implementation of this research is executed using MATLAB software. The simulation results are compared with the various conventional methods in terms of correlation coefficient (PLCC), Root Mean Square Error (RMSE), Spearman Rank Order Correlation Coefficient (SROCC) and Kendall Rank Order Correlation Coefficient (KROCC), and Mean Absolute Error (MAE). In addition, our proposed approach yielded a quality number approximately we achieved significant 20% improvement than existing methods, with the PLCC parameter showing a notable increase compared to current techniques. Moreover, the RMSE number decreased by 12% when compared to existing methods. Graphical representations indicated mean MAE values of 0.02 for MRI knee dataset, 0.09 for MRI brain dataset, and 0.098 for MRI breast dataset, showcasing significantly lower MAE values compared to the baseline models.

Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study

Rationale and objectivesTo determine the role of dynamic contrast-enhanced (DCE) MRI-radiomics in predicting the International Society of Urological Pathology Grade Group (ISUP-GG) in therapy-naïve prostate cancer (PCa) patients. Materials and MethodsIn this ethics review board-approved retrospective study on two prospective clinical trials between 2017 and 2020, 73 men with suspected/confirmed PCa were included. All participants underwent multiparametric MRI. On MRI, dominant lesions (per PI-RADS) were identified. DCE-MRI radiomic features were extracted from the segmented volumes following the image biomarker standardization initiative (IBSI) guidelines through 14 time points. Histopathology evaluation on the cognitive-fusion targeted biopsies was set as the reference standard. Univariate regression was done to evaluate potential predictors across all calculated features. Random forest imputation was used for multivariate modelling. ResultsSeventy-three index lesions were reviewed. Histopathology revealed 28, 16, 13 and 16 lesions with ISUP-GG-Negative/1/2, ISUP-GG-3, ISUP-GG-4 and ISUP-GG-5, respectively. From the extracted features, total lesion enhancement (TLE), minimum enhancement intensity and Grey-Level Run Length Matrix (GLRLM) were the most significantly different parameters among ISUP-GGs (Neg/1/2 vs 3/4 vs 5). Sixteen features with significant cross-sectional associations with ISUP-GGs entered the multivariate analysis. The final DCE partitioning model used only four features (lesion sphericity, TLE, GLRLM and Grey-Level Zone Length Matrix [GLZLM]). For the binarized diagnosis (ISUP-GG≤2 vs ISUP-GG>2), the accuracy reached 81%. ConclusionDCE-MRI radiomics might be used as a non-invasive tool for aiding pathological grade group prediction in therapy-naïve PCa patients, potentially adding complementary information to PI-RADS for supporting tailored diagnostic pathways and treatment planning.

DETECTION AND CLASSIFICATION OF COVID-19 USING GRAY-LEVEL FEATURES AND ENSEMBLE CLASSIFIER

The coronavirus or COVID-19 infectious virus is the deadliest and potentially dangerous disease for humans. Radiologists frequently employ medical imaging tools to visualize complex internal structures as well as the functioning of the body. With precise diagnosis, it is possible to identify the infectious COVID-19 virus earlier, especially in an individual having no visible symptoms. For the diagnosis and early detection of the infectious COVID-19 virus, chest X-rays (CXRs) have been utilized which are available at https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database . Applying the gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) feature extraction techniques, the features of the four classes (normal, lung opacity, viral pneumonia, and COVID-19) have been extracted and then classified by utilizing a machine learning (ML) classifier. Six distinct ML classifiers SMO (Sequential Minimal Optimization), Random Tree, MLP (Multi-Layer Perceptron), Linear SVM, Ensemble Classifier (Boosted Tree), and Bayes Net (Bayesian Network) with respective accuracy of 98.85%, 93.19%, 93.35%, 91.5%, 96.4%, and 96.454% are utilized to classify. The classifiers successfully distinguish between normal individuals, viral pneumonia-affected persons, lung opacity individuals, and COVID-19 virus-infected individuals who were considered for the study. These advanced technologies for coronavirus identification may be helpful in areas where access to skilled medical professionals and modern facilities is limited. Hence, as per the analysis, the study may be helpful in disease detection and classification. To classify the virus, radiologists’ second opinion can be quick and accurate in this urgent scenario.

CGSX Ensemble: An Integrative Machine Learning and Deep Learning Approach for Improved Diabetic Retinopathy Classification

This research proposes an integrated approach for automated diabetic retinopathy (DR) diagnosis, leveraging a combination of machine learning and deep learning techniques to extract features and perform classification tasks effectively. Through preprocessing of retinal images to enhance features and mitigate noise, two distinct methodologies are employed: machine learning feature extraction, targeting texture features like Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM), and deep learning feature extraction, utilizing pre-trained convolutional neural networks (CNNs) such as VGG, ResNet, or Inception. Following feature extraction, various classifiers, including Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines, are trained on the extracted features for DR classification. Alternatively, deep learning classifiers like CNNs or recurrent neural networks (RNNs) may be trained directly on the extracted features or on raw images. This comprehensive framework shows promising potential to improve the accuracy and efficiency of diabetic retinopathy (DR) diagnosis, enabling timely intervention and management of this vision-threatening condition.

NFS-23. ASSOCIATION OF LOCALIZED MAGNETIC RESONANCE IMAGING FEATURES IN ANTERIOR VISUAL PATHWAY WITH VISUAL ACUITY LOSS AMONG CHILDREN WITH NF1-OPG

Abstract BACKGROUND Half of children with optic pathway gliomas associated with neurofibromatosis type 1 (NF1-OPG) are at risk for vision acuity (VA) loss. NF1-OPGs manifest along the anterior visual pathway (AVP), where the overall shape and volume from MRI have been recently associated with VA loss. To investigate the added value of localized features within the optic nerves (ONs) and chiasm, we propose an automatic deep-learning framework for VA loss prediction from volumetric and radiomic analysis of ONs, chiasm, and AVP. METHODS We collected MRIs of 60 children with NF1-OPG from Children’s National Hospital (CNH, GE platform) and 75 from Children’s Hospital of Philadelphia (CHOP, Siemens platform). MRI sequences included T1-, T2-weighted and T2-FLAIR. Experts annotated the AVP to establish ground truth. Neuro-ophthalmic evaluation identified VA loss (LogMAR &amp;gt;= 0.2) in 52/135 children. Automatic AVP segmentation was performed using the Swin transformer network. ONs and chiasm were split through template-based registration. Brain volume, tumor location, child age, and 1,172 radiomic features were used to predict VA loss using support vector machines. Sequential feature selection associated with risk VA loss was done using sensitivity and univariate statistical tests (ANOVA). We compared predictions based on new ON and chiasm analysis with results based on AVP alone. RESULTS Balanced accuracy, sensitivity, specificity, and AUROC of VA loss prediction were 0.865, 0.882, 0.857, 0.899, respectively. Significant risk factors were maximal image intensity and gray level run length entropy in T2-FLAIR for ONs, and image intensity range in T2-FLAIR for chiasm. AUROC based on analysis of AVP alone dropped to 0.728 (p-value=0.028). CONCLUSIONS Deep learning-based analysis indicates an association of new localized MRI features in ONs and chiasm with VA loss, which show enhanced capability in predicting VA loss. This automated framework has the potential to accelerate and guide the treatment decisions for children with NF1-OPGs.

Emotion classification using electrocardiogram and machine learning: A study on the effect of windowing techniques

Automated emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in human–computer interaction, healthcare, and psychology. Electrocardiogram (ECG) signals are widely used due to their non-invasiveness, high temporal resolution, and direct relationship with the autonomic nervous system. In this study, we propose a novel approach for ECG-based emotion recognition using time-series to image encoding techniques and texture-based features combined with machine learning algorithms and deep learning architectures. The ECG data used in this study were obtained from the Continuously Annotated Signals of Emotion (CASE) and Wearable Stress and Affect Detection (WESAD) datasets. We categorized emotional states based on valence and arousal annotations into four classes: High-Valence High-Arousal, High-Valence Low-Arousal, Low-Valence High-Arousal, and Low-Valence Low-Arousal. The ECGs were segmented into 5 and 7-window segments and transformed into 2D representations using Gramian Angular Summation Field, Markov Transition Field, Recurrence Plot (RP), and the triple-channel fusion of all these images. Total of 85 textural features based on the Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Run Length Matrix, Zernike’s Moments, Hu’s Moments, Fractal Dimension Texture Analysis (FDTA), and First-Order Statistics were extracted. Classifiers, namely Random Forest, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), 1D Convolutional Neural Network (CNN), and Multi-head Attention Network were considered to classify the emotional states. The performance of the classifiers varied depending on the time-series to image encoding technique, segmentation approaches, and the classifier employed. We achieved the highest Weighted F-measure (F-m) of 94.91% (RP + XGB) and 86.78% (RP + SVM) using the 7-window and 5-window approaches, respectively. Our proposed 1D CNN architecture achieved the highest classification metrics (F-m = 92.52%, Balanced accuracy = 92.0%, Recall = 91.96%, and Precision = 93.16%) with RP images in a 7-window approach. GLCM and FDTA features made significant contributions to the classification of emotional states. Overall, our results suggest that the proposed method holds promise for developing more accurate and efficient emotion recognition systems.

Radiomic survival prediction with liver metastases in colorectal cancer.

e15584 Background: The survival outcome of colorectal cancer depends upon staging and the presence of metastases. Nonetheless, there isn’t a reliable prediction model for overall survival (OS) specifically tailored to patients with colorectal cancer who have liver metastases. Consequently, this study aims to find radiomic features that can be utilized to predict OS of colorectal cancer with liver metastases. Methods: We utilized the Colorectal Liver Metastases data set in The Cancer Imaging Archive (TCIA). Overall, 197 patients with colorectal cancer metastatic to the liver were identified. Semi-automatic segmentation of liver metastases was used to extract radiomic features including Gray-Level Co-occurrence (glcm), Gray-Level Run Length Matrix (glrlm), Gray-Level Size Zone Matrix (glszm), Gray-Level Dependence Matrix (gldm), and Neighborhood Gray-Tone Difference Matrix (ngtdm). 3D Slicer was used to extract radiomic features. We tested a Cox proportional hazards model in each radiomics feature to predict OS. Results: Total 107 radiomic features were extracted from pre-segmented liver metastases on patients’ Computed Tomography images. Among them, original shape sphericity, original glcm correlation, original gldm DependenceEntropy, and original glrlm RunEntropy were statistically significant for the prediction of OS, with a hazard ratio of -2.16 (CI 0.01-0.92; p value 0.04), 1.54 (CI 1.5-14.0; p value 0.006), 0.51 (CI 1.0-2.5; p value 0.02), and 0.52 (CI 1.0-2.6; p value 0.03), respectively. Conclusions: Our data analysis indicates crucial radiomic features that hold promise for predicting the clinical outcome of patients with colorectal cancer who have liver metastases. Future research with a larger sample size is warranted for the external validation of our results.

An Ensemble Classification Model to Predict Alzheimer’s Incidence as Multiple Classes

This study introduces an ensemble classification model designed to categorize Alzheimer’s disease (AD) into four distinct classes—mild dementia, no dementia, moderate dementia, and very mild dementia—using Magnetic Resonance Imaging (MRI). The proposed model entitled the Ensemble Classification Model to Predict Alzheimer's Incidence as Multiple Classes (PAIMC) that integrates a six-dimensional analysis of MR images, encompassing entropies, Fractal Dimensions, Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), morphological features, and Local Binary Patterns. A four-fold multi-label cross-validation approach was employed on a benchmark dataset to evaluate the model's performance. Quantitative analysis reveals that PAIMC consistently achieves superior Decision Accuracy, F-Score, Specificity, Sensitivity Recall, and Precision metrics compared to existing state-of-the-art models. For instance, PAIMC's Decision Accuracy and Precision outperform the second-best model by a notable margin across all folds. The model also demonstrates a significant improvement in Sensitivity Recall and Specificity, reinforcing its efficacy in the multi-class classification of AD stages. A novel data diversity assessment measure was developed and utilized, further confirming the robustness of the PAIMC model. The results underscore the potential of PAIMC as a highly accurate tool for AD classification in clinical settings.