Classification Method Research Articles

Detecting rhythmic spiking through the power spectra of point process model residuals.

Objective.Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ("RP", the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established "shuffling" procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.Approach.In a novel "residuals" method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the preceding nrmilliseconds. Finally, we compute the PSD of the model's residuals.Main results.We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey -- in which alpha-beta oscillations (8-30 Hz) were anticipated -- the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.Significance.These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.

Quantitative Assessment of Geosites and Mine Heritage as a Resource: The Case Study of Lungro Salt Mine (Calabria, Italy)

In this work, the old Lungro mining site was studied by applying two classification methods (Brilha and IELIG Method), considering the site-specific scientific, educational, touristic, and degradation aspects, to promote it as a geosite. The results were compared with those from other worldwide areas, and a potential re-evaluation in the tourism sector was suggested, considering historical data and geological issues. The methods used evaluate the Lungro site an average score from a scientific perspective, and from high to very high for the tourist-educational aspects. On the contrary, lower scores were found regarding the degradation of the site due to natural phenomena, led to a lower total score. The obtained results are comparable with other similar restored mining areas, located in various parts of the world which today represent important geotourists sites recognized internationally.Based on these results, the safest, most cost-effective, and straightforward way to preserve and restore the site is by creating a “Geotourism Route.“. This choice would increase awareness of the area, providing the general public, including the curious and “non-experts”, with an understanding of various geological processes and the extensive history of the mine. A campaign to promote and preserve the Lungro site as a geoheritage will be encouraged, with significant implications for tourism at the local, national, and international levels.

Functional Characterization of the Human BRCA1 ∆11 Splicing Isoforms in Yeast

BRCA1, a crucial tumor suppressor gene, has several splicing isoforms, including Δ9–11, Δ11, and Δ11q, which lack exon 11, coding for significant portions of the protein. These isoforms are naturally present in both normal and cancerous cells, exhibiting altered activity compared to the full-length BRCA1. Despite this, the impact on cancer risk of the germline intronic variants promoting the exclusive expression of these Δ11 isoforms remains uncertain. Consequently, they are classified as variants of uncertain significance (VUS), posing challenges for traditional genetic classification methods due to their rarity and complexity. Our research utilizes a yeast-based functional assay, previously validated for assessing missense BRCA1 variants, to compare the activity of the Δ11 splicing isoforms with known pathogenic missense variants. This approach allows us to elucidate the functional implications of these isoforms and determine whether their exclusive expression could contribute to increased cancer risk. By doing so, we aim to provide insights into the pathogenic potential of intronic VUS-generating BRCA1 splicing isoforms and improve the classification of BRCA1 variants.

Class-Irrelevant Feature Removal for Few-Shot Image Classification.

Most existing few-shot image classification methods employ global pooling to aggregate class-relevant local features in a data-drive manner. Due to the difficulty and inaccuracy in locating class-relevant regions in complex scenarios, as well as the large semantic diversity of local features, the class-irrelevant information could reduce the robustness of the representations obtained by performing global pooling. Meanwhile, the scarcity of labeled images exacerbates the difficulties of data-hungry deep models in identifying class-relevant regions. These issues severely limit deep models' few-shot learning ability. In this work, we propose to remove the class-irrelevant information by making local features class relevant, thus bypassing the big challenge of identifying which local features are class irrelevant. The resulting class-irrelevant feature removal (CIFR) method consists of three phases. First, we employ the masked image modeling strategy to build an understanding of images' internal structures that generalizes well. Second, we design a semantic-complementary feature propagation module to make local features class relevant. Third, we introduce a weighted dense-connected similarity measure, based on which a loss function is raised to fine-tune the entire pipeline, with the aim of further enhancing the semantic consistency of the class-relevant local features. Visualization results show that CIFR achieves the removal of class-irrelevant information by making local features related to classes. Comparison results on four benchmark datasets indicate that CIFR yields very promising performance.

A Hybrid Machine Learning Approach for Intrusion Detection and Mitigation on IoT Smart Healthcare

Strong cybersecurity solutions are becoming more and more important as Internet of Things (IoT) technology integration in healthcare settings develops. This study offers a method for feature extraction, selection, and attack classification by fusing the discriminative capacity of feedforward neural networks (FNNs) with the adaptability of fuzzy logic systems. In delicate healthcare database of IoT wearable devices, to reduce false alarm and guaranteeing intrusion detection dependability are the main priorities. The suggested method uses a feature extraction, selection technique, training and testing based on FNN, which allows the model to adjust to the dynamic and varied character of medical data. During the assessment stage, a dataset including a range of healthcare IoT scenarios, including different kinds of attacks, is used to train and evaluate the model, the ToN_IoT dataset was used. Fuzzy logic improves the system's resilience in identifying pertinent features by managing uncertainties and imprecise input. Fuzzy logic is one of the best technique for handling uncertainty, its linguistic representation and rule reasoning helps in better identification and classification. The findings indicate a noteworthy decrease in the frequency of false alarms when juxtaposed with conventional intrusion detection systems. Results obtained from the model are 99.2, 98.8, 99.5, 99.1 & 0.008 for accuracy, precision, recall, F1-Score and False alarm respectively. Promising outcomes in protecting IoT healthcare environments are demonstrated by the suggested system, opening the door to better patient data privacy and system resilience against cyberattacks.

The predictive role of lung clearance index on FEV<sub>1</sub> decline in cystic fibrosis

Background. The lung clearance index (LCI) is a sensitive lung function index that is used to detect early lung disease changes in children with cystic fibrosis (CF). This study aimed to define the predictive role of baseline LCI, along with other potential factors on the change in forced expiratory volume in one second (FEV1) during one-year follow-up in CF patients who had a percent predicted (pp) FEV1≥80. Methods. LCI was concurrently performed on 57 CF patients who had ppFEV1 ≥80 at month zero. The ppFEV1 decline was evaluated prospectively during the one year follow up. The primary outcome of ppFEV1 decline in the study group in one year was dichotomized according to the median value for the decline in ppFEV1, which was 3.7. The LCI value predicting ppFEV1 decline at the end of one year was calculated with receiver operating characteristic curve analysis. Regression analysis was performed. Furthermore, a decision tree was constructed using classification and regression tree methods to better define the potential effect of confounders on the ppFEV1 decline. Results. The LCI value for predicting ppFEV1 decline >3.7% at the end of one year was 8.2 (area under the curve: 0.80) Multivariable regression analysis showed that the absence of the F508del mutation in at least one allele, LCI >8.2 and initial FEV1 z-score were predictors of a ppFEV1 decline >3.7 (p<0.001). Factors altering ppFEV1 decline>3.7% at the end of one-year evaluated by decision trees were as follows: initial FEV1 z-score, type of CFTR mutation, LCI value and initial weight-for-age z-score. Conclusions. LCI is sensitive for predicting ppFEV1 decline in patients with ppFEV1 ≥80 along with the initial FEV1-z-score and type of CFTR mutation.

Explainable artificial intelligence for LDL cholesterol prediction and classification

IntroductionMonitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C is critical. The present study aims to evaluate Artificial Intelligence (AI) and Explainable AI (XAI) methodologies in predicting LDL-C levels while emphasizing the interpretability of these predictions. Materials and methodsWe retrospectively reviewed data from the Laboratory Information System (LIS) of Ankara Etlik City Hospital (AECH). We included 60.217 patients with standard lipid profiles (total cholesterol [TC], high-density lipoprotein cholesterol, and triglycerides) paired with same-day direct LDL-C results. AI methodologies, such as Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), were used to predict LDL-C and compared directly measured and calculated LDL-C with formulas. XAI techniques such as Shapley additive annotation (SHAP) and locally interpretable model-agnostic explanation (LIME) were used to interpret AI models and improve their explainability. ResultsPredicted LDL-C values using AI, especially RF or GB, showed a stronger correlation with direct measurement LDL-C values than calculated LDL-C values with formulas. TC was shown to be the most influential factor in LDL-C prediction using SHAP and LIME. The agreement between the treatment groups based on NCEP ATPIII guidelines according to measured LDL-C and the LDL-C groups obtained with AI was higher than that obtained with formulas. ConclusionsIt can be concluded that AI is not only a reliable method but also an explainable method for LDL-C estimation and classification.

Alpine Catchments’ Hazard Related to Subaerial Sediment Gravity Flows Estimated on Dominant Lithology and Outcropping Bedrock Percentage

Sediment gravity flows (SGFs) cause serious damage in the Alpine regions. In the literature, several methodologies have been elaborated to define the main features of these phenomena, mainly considering the rheological features of the flow processes by laboratory experiments or by flow simulation using 2D or 3D propagation models or considering a single aspect, such as the morphometric parameters of catchments in which SGFs occur. These very targeted approaches are primarily linked to the definition of SGFs’ propagation behavior or to identify the predisposing role played by just one feature of catchments neglecting other complementary aspects regarding phenomena and the environment in which SGFs can occur. Although the research aimed at the quantification of some parameters that drive the behavior of SGFs provides good results in understanding the flow mechanisms, it does not provide an exhaustive understanding of the overall nature of these phenomena, including their trigger conditions and a complete view of predisposing factors that contribute to their generation. This paper presents a research work based on the collection and cross-analysis of lithological, geomechanical, geomorphological and morphometrical characteristics of Alpine catchments compared with sedimentological and morphological features of SGF deposits, also taking in to account the rainfall data correlation with historical SGF events. A multidisciplinary approach was implemented, aiming at quantifying SGF causes and characteristics starting from the catchments’ features where the phenomena originate in a more exhaustive way. The study used 78 well-documented catchments of Susa Valley (Western Italian Alps), having 614 historical flow events reported, that present a great variability in geomorphological and geological features. As the main result, three catchment groups were recognized based on the dominant catchment bedrock’s lithology characteristics that influence the SGFs’ rheology, sedimentological and depositional features, triggering rainfall values, seasonality, occurrence frequency and alluvial fan architecture. The classification method was also compared with the catchments’ morphometry classification, demonstrating that the fundamental role in determining the type of flow process that can most likely occur in a given catchment is played by the bedrock outcropping percentage, regardless of the results provided by the morphometric approach. The analysis of SGF events through the proposed method led to a relative estimate of the hazard degree of these phenomena distinguished by catchment type.

A new method for predicting precipitation δ18O distribution based on deep learning and spatio-temporal clustering

ABSTRACT Predicting precipitation δ18O accurately is crucial for understanding water cycles, paleoclimates, and hydrological applications. Yet, forecasting its spatio-temporal distribution remains challenging due to complex climate interactions and extreme events. We developed a method combining spatio-temporal clustering and deep learning neural networks to improve multi-site, multi-year precipitation δ18O predictions. Using a comprehensive dataset from 33 German sites (1978-2012), our model considers precipitation δ18O and its controlling factors, including precipitation and temperature distribution. We applied the K-means++ method for classification and divided data into training and prediction sets. The CNN (Convolutional Neural Network) model extracted spatial features, while the Bi-LSTM (Bi-directional Long Short-Term Memory) model focused on temporal features. Spatio-temporal clustering using K-means++ improved forecast accuracy and reduced errors. This study highlights the potential of deep learning and clustering techniques for forecasting complex spatio-temporal data and offers insights for future research on isotope distributions.

Investigating the Effect of Class Balancing Methods on the Performance of Machine Learning Techniques: Credit Risk Application

Credit risk arises as a result of the failure of the loans given by banks to the customers to fulfill their obligations at the end of the specified term. Technological advances allow the use of machine learning methods in various sectors. These methods aim to facilitate the identification of customers at risk with the system adapted to the creditworthiness processes of banks. For this purpose, in order to make the most appropriate evaluation in the lending process of banks, re-sampling techniques to eliminate the problem of class imbalance encountered in unbalanced data sets were made balanced and their effects on machine learning were investigated. During the implementation phase, German, Australian and HMEQ credit data sets were used. Different machine learning classification methods such as Logistic Regression (LR), K-Narest Neighbor (KNN), Naive Bayes (NB), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Decision Trees (DT), Random Forests (RF), Gradient Boosting Decision Trees (GBDT), Extremely Randomized Trees, Hard and Soft Voting were used to detect risky customers. The problem of class imbalance was balanced with resampling and hybrid techniques such as Random Oversampling (ROS), Random Undersampling (RUS), Balanced Bagging Classifier (BBC), SMOTE-Tomek Links and SMOTE-ENN. In this context, the performances of three different data sets were examined in four different scenarios. As a result of the study, the hybrid method, in which oversampling and undersampling methods are used together for the class balancing problem, showed the best classification performance among machine learning techniques.

Depth-based statistical analysis in the spike train space

Metric-based summary statistics such as mean and covariance have been introduced in neural spike train space. They can properly describe template and variability in spike train data, but are often sensitive to outliers and expensive to compute. Recent studies also examine outlier detection and classification methods on point processes. These tools provide reasonable result, whereas the accuracy remains at a low level in certain cases. In this study, we propose to adopt a well-established notion of statistical depth to the spike train space. This framework can naturally define the median in a set of spike trains, which provides a robust description of the ‘template’ of the observations. It also provides a principled method to identify ‘outliers’ and classify data from different categories. We systematically compare the new median, outlier detection and classification tools with state-of-the-art competing methods. The result shows the median has superior description for template than the mean. Moreover, the proposed outlier detection and classification perform more accurately than previous methods.

Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony’s unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern.Key points• Individual bacteria cells are placed on gradient agar plates by a cell sorter• Laser-light scatter patterns are used to recognize antibiotic-resistant organisms• Scatter patterns formed by colonies correspond to AMR-associated phenotypes

Hitac: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2

BackgroundFungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.ResultsHere we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.ConclusionsHiTaC is publicly available at the Python package index, BIOCONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available at https://gitlab.com/dacs-hpi/hitac.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient’s health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

A new classification for emergency critically ill patients and analysis of their adverse events during intrahospital transport: A cluster analysis.

Critical patients may experience various adverse events during transportation within hospitals. Therefore, quickly evaluating and classifying patients before transporting them from the emergency department and focusing on managing high-risk patients are critical. At present, no unified classification method exists; all the current approaches are subjective. To ensure transportation safety, we conducted a cluster analysis of critically ill patients transferred from the emergency department to the intensive care unit. Single-centre cohort study. This study was conducted at a comprehensive first-class teaching hospital in Beijing. Convenience sampling and continuous enrolment were employed. We collected data from 1 January 2019, to 31 December 2021. All patients were transferred from the emergency department to the intensive care unit, and cluster analysis was conducted using five variables. A total of 584 patients were grouped into three clusters. Cluster 1 (high systolic blood pressure group) included 208 (35.6%) patients. Cluster 2 (high heart rate and low blood oxygen group) included 55 (9.4%) patients. Cluster 3 (normal group) included the remaining 321 (55%) patients. The oxygen saturation levels of all the patients were lower after transport, and the proportion of adverse events (61.8%) was the highest in Cluster 2 (p < .05). This study utilized data on five important vital signs from a cluster analysis to explore possible patient classifications and provide a reference for ensuring transportation safety. Before transferring patients, we should classify them and implement targeted care. Changes in blood oxygen levels in all patients should be considered, with a focus on the occurrence of adverse events during transportation among patients with high heart rates and low blood oxygen levels.

Comprehensive Classification of the Capitellar Injury Concurrent with Radial Head Fracture.

Capitellar injury (CI) includes capitellar cartilage injury (CCI) and capitellar fracture (CF). A comprehensive classification of CI concurrent with radial head fracture (RHF) that can guide surgical strategy is lacking in the literature. Therefore, this study aimed to introduce a comprehensive classification of CI concurrent with RHF and investigate its value. A total of 35 patients with CI concurrent with RHF confirmed by surgical exploration were retrospectively analyzed, includingmales in 19 cases and females in 16 cases. RHF was classified according to the Mason classification, and CI was classified into six types, including 3 types of CCI and CF, each based on the site and degrees of injuries (comprehensive classification method proposed in this study). The classification results were analyzed. Two radiologists were selected to independently classify the CI, and the inter- and intra-observer agreements were analyzed with kappa statistics. Mason Type I, II, III, and IV RHF accounted for 14.3%, 48.6%, 37.1%, and 0% of cases, respectively. Type I, II, III, IV, V, and VI CIs accounted for 22.9%, 34.3%, 25.7%, 11.4%, 2.9%, and 2.9% of cases, respectively. Therewas no obvious relationship between the CI and RHF types (p > 0.05). All Type I CIs underwent removal, 9 Type II CIs underwent microfracture repair, and 3 Type II CIs underwent removal. All Type III CIs underwent fixation, one Type IV CI underwent removal, and 3 Type IV CIs underwent fixation, one Type V CI underwent fixation, and one Type VI CI underwent arthroplasty. The inter- and intra-observer kappa coefficients were 0.830 ~ 0.905 and 0.805 ~ 0.892, respectively. At 12 months postoperatively, the elbow function evaluated by MEPS was 91, with an excellent and good rate of 97%. Different types of CI differ not only in pathology but also in treatment methods. The CI comprehensive classification put forth in this paper for the first time reflects different types of pathology well, with high consistency and repeatability, and can guide the selection of surgical methods, leading to satisfactory postoperative results.

Using experience classification for training non-Markovian tasks

Unlike standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, which requires long-term memory and dependency. Hence solving a non-Markovian task is more difficult and challenging than solving a Markovian one. In this paper, we propose a novel RL approach for training non-Markovian tasks expressed in temporal logic LTLf (Linear Temporal Logic over Finite Traces). To this end, an encoding of linear complexity from LTLf into MDPs (Markov Decision Processes) is introduced in order to take advantage of advanced RL algorithms. We further propose an experience classification method based on the automaton structure (theoretically equivalent to LTLf specifications). An automatic reward shaping technique and a prioritized experience replay mechanism are developed to cooperate with the classification method to improve the performance of RL algorithms. We provide empirical evaluations on two widely used benchmark problems, Waterworld and Cartpole, both augmented with complex non-Markovian tasks. The evaluations are conducted with respect to the metrics of training speed, policy quality, convergence rates, computational efficiency and scalability etc. The experimental results show that our approach achieves superior performance over other relevant studies, specially with an average improvement of 133% in convergence rate and a reduction of 11% in training time.

Brain tumour detection via EfficientDet and classification with DynaQ-GNN-LSTM

The early detection and accurate staging of brain tumors are critical for effective treatment strategies and improving patient outcomes. Existing methods for brain tumor classification often struggle with limitations such as suboptimal precision, accuracy, and recall rates, alongside significant delays in processing. The current methodologies in brain tumor classification frequently encounter issues such as inadequate feature extraction capabilities and limited accuracy in segmentation, which impede their effectiveness. To address these challenges, the proposed model integrates Fuzzy C-Means for segmentation, leveraging its ability to enhance the accuracy in distinguishing tumor regions. Bounding boxes surrounding identified tumour regions are produced by the method by efficiently utilising calculated region attributes. The use of Vision Transformers for feature extraction marks a significant advancement, offering a more nuanced analysis of the intricate patterns within brain imaging data samples. These features are then classified using a Dyna Q Graph LSTM (DynaQ-GNN-LSTM), a cutting-edge approach that combines the strengths of deep learning, reinforcement learning, and graph neural networks. The superiority of the proposed model is evident through its performance on multiple datasets. It demonstrates an 8.3% increase in precision, 8.5% increase in accuracy, 4.9% increase in recall and 4.5% increase in specificity, alongside 2.9% reduction in delay compared to existing methods. In conclusion, the proposed method offers an efficient solution to the challenges faced in brain tumor classification. The study's findings underscore the transformative impact of integrating cutting-edge technologies in medical diagnostics, paving the way for more accurate, and timely health interventions for clinical scenarios.

Multivariate technique for the Prediction and Classification of Brain Tumor using deep shallow network

Brain tumors, a major cause of death, carry a substantial socio-economic burden. It is essential to distinguish between various types of tumors, such as gliomas, meningiomas, and pituitary tumors, using MRI data. This assists radiologists and eliminates the need for risky histology biopsies. The objective of the proposed work is to create an automated system capable of distinguishing tumor types. This system integrates a deep, shallow network with the ResNet18 model for feature extraction and machine learning methods for classification on images from the Kaggle dataset. The standalone ResNet18 model produced an accuracy of 91.42% and to enhance the accuracy, features extracted from the pool5 layer of the ResNet18 architecture were examined by chi-square algorithm. The optimal deep features extracted using the chi-square algorithm were integrated with 17 machine learning classifier techniques comprising variants of SVM, KNN, and ensemble techniques. The proposed method predicted the classes, yielding an accuracy of 99.9% for cubic SVM among the variants of SVM, 94% for cosine KNN among the variants of KNN, and 99.8% for the Adaboost algorithm only, with a maximum of the top 10 features justified using local interpretable model-agnostic (LIME) and gradient-weighted class activation mapping (Grad-CAM) visualization techniques.

A new index for cervical curvature evaluation - relative cervical curvature area

ObjectiveTo put forward a new index of cervical curvature evaluation - relative cervical curvature area,and a new classification of cervical spine was proposed according to the relative cervical curvature area. MethodsA total of 167 subjects with cervical spondylosis were included in the study. Firstly, 119 subjects were selected to measure C2-C7 lordosis angle by Cobb angle method, Harrison posterior tangent method and Jackson physiological stress line method, and then their relative cervical curvature area, C1-C7 Cobb angle, C7 slope and T1 slope were measured.The correlation between relative cervical curvature area and three measurement methods and common sagittal parameters was analyzed.According to the angle classification method, we calculated the diagnostic boundary value of the relative cervical curvature area classification, and selected 48 subjects to evaluate its diagnostic efficacy.Finally, 119 subjects were re-evaluated according to the diagnostic threshold and the number of intersections to verify the feasibility of the new classification. ResultsThe results showed that the relative cervical curvature area index had good intra-observer and inter-observer repeatability. Relative cervical curvature area was correlated with Harrison posterior tangent method ( r = 0.930 ), Cobb angle method ( r = 0.886 ) and Jackson physiological stress line method ( r = 0.920 ), and correlated with C1-C7 Cobb angle, C7 slope and T1 slope.The relative cervical curvature area has a good diagnostic performance for distinguishing patients with lordosis, straightening and kyphosis.According to the new classification of cervical spine, 119 subjects were divided into 57 simple lordosis, 11 simple straightening, 4 simple kyphosis, 26 S-type and 21 RS-type. ConclusionThe relative cervical curvature area uses the area parameter instead of the original angle parameter and distance parameter to incorporate the change of segmental curvature, which makes up for the shortcomings of the Cobb angle method that only evaluates the curvature of two vertebrae, and better reflects the cervical curvature.Studies have shown that relative cervical curvature area has good repeatability and diagnostic value, and found that it has a good correlation with common cervical sagittal parameters.The new classification of cervical spine makes up for the disadvantage that the angle classification method cannot distinguish between S-type and RS-type, and initially proposes to use the number of intersections and the relative absolute value area to reflect the severity of S-type.