Early Classification Research Articles

Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7–30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.

Cell penetrating peptides: Highlighting points in cancer therapy.

Cell-penetrating peptides (CPPs), first identified in HIV a few decades ago, deserved great attention in the last two decades; especially to support the penetration of anticancer drug means. In the drug delivery discipline, they have been involved in various approaches from mixing with hydrophobic drugs to the use of genetically conjugated proteins. The early classification as cationic and amphipathic CPPs has been extended to a few more classes such as hydrophobic and cyclic CPPs so far. Developing potential sequences utilized almost all methods of modern science: choosing high-efficiency peptides from natural protein sequences, sequence-based comparison, amino acid substitution, obtaining chemical and/or genetic conjugations, in silico approaches, in vitro analysis, animal experiments, etc. The bottleneck effect in this discipline reveals the complications that modern science faces in drug delivery research. Most CPP-based drug delivery systems (DDSs) efficiently inhibited tumor volume and weight in mice, but only in rare cases reduced their levels and continued further processes. The integration of chemical synthesis into the development of CPPs made a significant contribution and even reached the clinical stage as a diagnostic tool. But constrained efforts still face serious problems in overcoming biobarriers to reach further achievements. In this work, we reviewed the roles of CPPs in anticancer drug delivery, focusing on their amino acid composition and sequences. As the most suitable point, we relied on significant changes in tumor volume in mice resulting from CPPs. We provide a review of individual CPPs and/or their derivatives in a separate subsection.

Classification of Breast Masses Using Ultrasound Images by Approaching GAN, Transfer Learning and Deep Learning Techniques

Breast cancer is a common cause of death among women worldwide. Ultrasonic imaging is a valuable diagnostic tool in breast cancer detection. However, the accuracy of computer-aided diagnosis systems for breast cancer classification is limited due to the lack of well-annotated datasets. This study proposes a deep learning-based framework for breast mass classification using ultrasound images, which incorporates a novel data augmentation technique, Generative Adversarial Network (GAN), and Transfer Learning (TL). Automating early tumor identification and classification in breast cancer diagnosis can save lives by improving the accuracy of diagnoses and reducing the need for invasive procedures. However, the limited availability of well-annotated datasets for ultrasound images of breast cancer has hampered the development of accurate computer-aided diagnosis systems. The accuracy of breast mass classification using ultrasound images is limited due to the lack of well-annotated datasets. Conventional data augmentation techniques have limitations in applications with strict guidelines, such as medical datasets. Therefore, there is a need to develop a novel data augmentation technique to improve the accuracy of breast mass classification using ultrasound images. The proposed framework can be extended to other medical imaging applications, where the availability of well-annotated datasets is limited. The GAN-based data augmentation technique and TL-based feature extraction can be used to improve the accuracy of classification models in other medical imaging applications. Additionally, the proposed framework can be used to develop accurate computer-aided diagnosis systems for breast cancer detection in clinical settings. The proposed framework incorporates a deep learning-based approach for breast mass classification using ultrasound images. The framework includes a GAN-based data augmentation technique and TL for feature extraction. The dataset used for training and testing the model is the Breast Ultrasound Images (BUSI) dataset, which includes 1311 images with normal and abnormal breast masses. The proposed framework achieved an accuracy of 99.6% for breast mass classification using ultrasound images, which outperformed existing methods. The GAN-based data augmentation technique and TL-based feature extraction improved the accuracy of the classification model. The results suggest that deep learning algorithms can be effectively applied for breast ultrasound categorization. The proposed framework presents a novel approach for breast mass classification using ultrasound images, which incorporates a GAN-based data augmentation technique and TL-based feature extraction. The results demonstrate that the proposed framework outperforms existing methods and achieves high accuracy in breast mass classification using ultrasound images. This framework can be useful for developing accurate computer-aided diagnosis systems for breast cancer detection.

Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures.

The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify the severity of SAD into different grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks. We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC). PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM). Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.

Oral lesions in human monkeypox disease and their management.

The current outbreak of human monekypox (MPX) in several endemic and non-endemic regions in 2022 has generated significant international attention. Despite the early classification as zoonotic, MPXV has demonstrated the potential for human-to-human transmission through close contact with lesions, body fluids, respiratory droplets, and contaminated materials. Therefore, our objective was to elaborate on the oral lesions in human MPX and their management. Articles published up to August, 2022, were screened to identify relevant studies in humans that reported oral lesions in MPX. Oral lesions have been found to manifest differently and transform from vesicles to pustules, accompanied by umbilication and crusting within four weeks. Along with fever and lymphadenopathy, these lesions may develop in the oral cavity and then spread to the skin surrounding the extremities in a centrifugal pattern. In some patients, the oropharyngeal and perioral lesions were the initial presentations. The oral lesions of MPX infection and its management strategies are relevant for dentists. Dental practitioners may be the first to detect the initial lesions of MPX. Therefore, high alertness should be there, especially while examining patients with fever and lymphadenopathy. It is also essential to thoroughly examine the oral cavity for macular and papular lesions in oral mucosa, tongue, gingiva, and epiglottis. Symptomatic and supportive care of oral lesions is recommended.

Skin Melanoma Cancer Detection and Classification using Machine Learning

Skin cancer is a common and potentially life-threatening disease that affects millions of people worldwide. Early detection and accurate classification of skin lesions are critical for effective therapy and improved patient outcomes. In recent years, advances in machine learning and computer vision techniques have shown promising results in automating skin cancer detection and classification. The goal of this project is to develop an automated system for skin cancer detection and classification using machine learning algorithms. The proposed system uses a dataset of dermatoscopic images collected from different sources covering different types of skin lesions, including malignant melanoma, basal cell carcinoma, and squamous cell carcinoma. The project includes several main stages. First, preprocessing techniques including noise reduction, normalization, and feature extraction are used to improve image quality. Next, a comprehensive set of features such as color, texture, and portrait features are extracted from the preprocessed images. These features are input to various machine learning models, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs) and Random Forests.

Towards a real-time IoT: Approaches for incoming packet processing in cyber–physical systems

Embedded real-time devices for monitoring, controlling, and collaboration purposes in cyber–physical systems are now commonly equipped with IP networking capabilities. However, the reception and processing of IP packets generates workloads in unpredictable frequencies as networks are outside of a developer’s control and difficult to anticipate, especially when networks are connected to the internet. As of now, embedded network controllers and IP stacks are not designed for real-time capabilities, even when used in real-time environments and operating systems.Our work focuses on real-time aware packet reception from open network connections, without a real-time networking infrastructure. This article presents two experimentally evaluated modifications to the IP processing subsystem and embedded network interface controllers of constrained IoT devices. The first, our software approach, introduces early packet classification and priority-aware processing in the network driver. In our experiments this allowed the network subsystem to remain active at a seven-fold increase in network traffic load before disabling the receive interrupts as a last resort. The second, our hardware approach, makes changes to the network interface controller, applying interrupt moderation based on real-time priorities to minimize the number of network-generated interrupts. Furthermore, this article provides an outlook on how the software and hardware approaches can be combined in a co-designed packet receive architecture.

Longitudinal traumatic peripheral nerve injury recovery: quantitative description, classificationand prediction.

Aim: Repair of peripheral nerves is recommended following transection. Systematic evaluation of longitudinal recovery in injury models is needed to improve patient management. Gompertz function provided straightforward interpretation and prediction of recovery outcomes. Materials & methods: Behavioural sciatic function index, measured 3days postinjury, and weekly for 12weeks following full nerve transection and repair (n=6) as well as crush injuries (n=6). Results: Gompertz parametrization provided early classification between types of traumatic peripheral nerve injuries following surgical repair. Results distinguished injury nerves (A: p<0.01;Ti: p<0.05; Ic: p<0.05 and outcome: p<0.01). Early prognostication of outcomes (crush: 5.5±0.3 and cut/repair: 8±1weeks) preceded current methods. Conclusion: Our findings identify injury type, state of recovery and early prognostication of outcome.

YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification.

Pressure ulcers are significant healthcare concerns affecting millions of people worldwide, particularly those with limited mobility. Early detection and classification of pressure ulcers are crucial in preventing their progression and reducing associated morbidity and mortality. In this work, we present a novel approach that uses YOLOv5, an advanced and robust object detection model, to detect and classify pressure ulcers into four stages and non-pressure ulcers. We also utilize data augmentation techniques to expand our dataset and strengthen the resilience of our model. Our approach shows promising results, achieving an overall mean average precision of 76.9% and class-specific mAP50 values ranging from 66% to 99.5%. Compared to previous studies that primarily utilize CNN-based algorithms, our approach provides a more efficient and accurate solution for the detection and classification of pressure ulcers. The successful implementation of our approach has the potential to improve the early detection and treatment of pressure ulcers, resulting in better patient outcomes and reduced healthcare costs.

Early Detection and Classification of Heart Diseases by Employing IFCMML and 2L-C Model with I-GA Machine Learning Methods

Objectives: To identify a new method for early detection and classification of Heart Diseases (HD); to improve the accuracy of the results by employing I-FCMML (Improved Fuzzy C-Means Machine Learning) and 2L-C (Two-Level Classifier) models along with the I-GA (Improved Genetic Algorithm) methods. Methods: The I-FCMML algorithm is utilized for feature selection and extraction. Machine learning techniques such as Ensembled Random Forest method (ERFM) and Robust Gradient Boost Method (RGBT) are employed to predict the likelihood of HD and 2L-C, I-GA is used to classify and detect the HD at a premature stage based on features like age, gender, blood pressure, etc. IFCMML with 2L-C & I-GA extracts all the features from the dataset (Cleveland HD dataset from the Cleveland Clinic Foundation, Ohio, USA) which includes 303 observations, 14 features and selects the suitable function to perform disease classification and detection with high accuracy. To evaluate the performance of the proposed method, MATLAB is used for implementation. The results are compared with existing algorithms such as 3P-ANN, ANN-FAHP, ADWFS, EDSS, and FE-PCA. Findings: Early HD detection and classification is achieved with 96.02% accuracy, 95.80% sensitivity, 94.76% specificity, 95% precision, 94% recall, 0.90 True Positive, 0.87 True Negative, and 94.13% F-Score to detect and classify the HD in a robust manner, which is comparatively high than the existing methods. Novelty: According to the findings of the comprehensive study, the proposed new method I-FCMML with 2L-C & I-GA has the potential to provide accurate and competent detection and classification of HD at an early stage, which could help for timely treatment and management of HD patients, and it also outperforms the existing methods such as 3P-ANN, ANNFAHP, ADWFS, EDSS, and FE-PCA. Keywords: Heart Disease Detection; Classification Algorithm; Genetic Algorithm; Fuzzy C-Means ML; Image Processing

Blockchain-enabled healthcare monitoring system for early Monkeypox detection.

The recent emergence of monkeypox poses a life-threatening challenge to humans and has become one of the global health concerns after COVID-19. Currently, machine learning-based smart healthcare monitoring systems have demonstrated significant potential in image-based diagnosis including brain tumor identification and lung cancer diagnosis. In a similar fashion, the applications of machine learning can be utilized for the early identification of monkeypox cases. However, sharing critical health information with various actors such as patients, doctors, and other healthcare professionals in a secure manner remains a research challenge. Motivated by this fact, our paper presents a blockchain-enabled conceptual framework for the early detection and classification of monkeypox using transfer learning. The proposed framework is experimentally demonstrated in Python 3.9 using a monkeypox dataset of 1905 images obtained from the GitHub repository. To validate the effectiveness of the proposed model, various performance estimators, namely accuracy, recall, precision, and F1-score, are employed. The performance of different transfer learning models, namely Xception, VGG19, and VGG16, is compared against the presented methodology. Based on the comparison, it is evident that the proposed methodology effectively detects and classifies the monkeypox disease with a classification accuracy of 98.80%. In future, multiple skin diseases such as measles and chickenpox can be diagnosed using the proposed model on the skin lesion datasets.

DFCV: a framework for evaluation deep learning in early detection and classification of lung cancer

DFCV: a framework for evaluation deep learning in early detection and classification of lung cancer

SURVEY OF BLOOD CANCER DETECTION AND CLASSIFICATION TECHNIQUES

Blood cancer is a complex and heterogeneous group of diseases that can affect the production and function of blood cells. Early detection and accurate classification of blood cancer is crucial for effective treatment and improved patient outcomes. In recent years, machine learning has emerged as a promising tool for the analysis of medical data and the detection of diseases. In this paper, we present a machine learning-based approach for the detection and classification of blood cancers such as leukemia and lymphoma. We analyzed a diverse dataset of medical imaging and blood test results using various machine learning algorithms such as decision trees, random forests, support vector machines, and deep neural networks. Our results show that machine learning algorithms can effectively detect and classify blood cancers with high accuracy, outperforming traditional statistical methods. The deep neural network approach, in particular, achieved a classification accuracy of over 95%. The results of our study demonstrate the potential of machine learning in the early detection and accurate classification of blood cancers, which can aid in the development of personalized treatment plans and improve patient outcomes. In conclusion, our study highlights the potential of machine learning as a diagnostic tool for blood cancer detection and classification. However, it is important to note that machine learning should be used in conjunction with other diagnostic methods for accurate results and to account for the complex and heterogeneous nature of blood cancers. Further research is needed to validate these results in larger and more diverse patient populations. Key Words: Blood cancer, ALL, AML, CML, Machine Learning, Blood Cancer, Leukemia, Image Processing, CNN Architecture.

Normalized Blood Flow Index in Optical Coherence Tomography Angiography Provides a Sensitive Biomarker of Early Diabetic Retinopathy.

To evaluate the sensitivity of normalized blood flow index (NBFI) for detecting early diabetic retinopathy (DR). Optical coherence tomography angiography (OCTA) images of healthy controls, diabetic patients without DR (NoDR), and patients with mild nonproliferative DR (NPDR) were analyzed in this study. The OCTA images were centered on the fovea and covered a 6 mm × 6 mm area. Enface projections of the superficial vascular plexus (SVP) and the deep capillary plexus (DCP) were obtained for the quantitative OCTA feature analysis. Three quantitative OCTA features were examined: blood vessel density (BVD), blood flow flux (BFF), and NBFI. Each feature was calculated from both the SVP and DCP and their sensitivities to distinguish the three cohorts of the study were evaluated. The only quantitative feature capable of distinguishing all three cohorts was NBFI in the DCP image. Comparative study revealed that both BVD and BFF were able to distinguish the controls and NoDR from mild NPDR. However, neither BVD nor BFF was sensitive enough to separate NoDR from the healthy controls. The NBFI has been demonstrated as a sensitive biomarker of early DR, revealing retinal blood flow abnormality better than traditional BVD and BFF. The NBFI in the DCP was verified as the most sensitive biomarker, supporting that diabetes affects the DCP earlier than SVP in DR. NBFI provides a robust biomarker for quantitative analysis of DR-caused blood flow abnormalities, promising early detection and objective classification of DR.

Screening of key pathogenic genes of type 1 diabetes in children.

The etiology of type 1 diabetes mellitus (T1DM) in pediatric populations remains poorly understood. The key to precise prevention and treatment of T1DM in identifying crucial pathogenic genes. These key pathogenic genes can serve as biological markers for early diagnosis and classification, as well as therapeutic targets. However, there is currently a lack of relevant research on screening key pathogenic genes based on sequencing data and efficient algorithms. The transcriptome sequencing results of peripheral blood mononuclear cells (PBMCs) of children with T1DM (GSE156035) were downloaded from the Gene Expression Omnibus (GEO) database. The data set contained 20 T1DM samples and 20 control samples. Differentially expressed genes (DEGs) in children with T1DM were selected based on fold change (FC) >1.5 times and adjusted P value <0.05. The weighted gene co-expression network was constructed. Hub genes were screened as modular membership (MM) >0.8 and gene significance (GS) >0.5. Intersection genes of DEGs and hub genes were defined as key pathogenic genes. The diagnostic efficacy of key pathogenic genes was evaluated using receiver operator characteristic (ROC) curves. A total of 293 DEGs were selected. Compared with the control group, 94 genes were down-regulated and 199 genes were up-regulated in the treatment group. Black modules (Cor =0.52, P=2e-12) were positively correlated with diabetic traits, whereas brown modules (Cor =-0.51, P=5e-12) and pink modules (Cor =-0.53, P=5e-13) were negatively correlated with diabetic traits. The black module contained 15 hub genes, the pink gene module contained 9 hub genes, and the brown module contained 52 hub genes. The intersection of hub genes and DEGs contained 2 genes, CCL25 and EGFR. The expression of CCL25 and EGFR was low in control samples and high in the test group (P<0.001). The areas under ROC curves (AUCs) of CCL25 and EGFR were 0.852 and 0.867, respectively (P<0.05). Weighted correlation network analysis (WGCNA) was used to identify the key pathogenic genes of T1DM in children, including CCL25 and EGFR, which have good diagnostic efficacy for T1DM in children.

An Efficient Investigation on Age-Related Macular Degeneration Using Deep Learning with Cloud-Based Teleophthalmology Architecture

AMD, or age-related macular degeneration, is the fourth most common visual ailment leading to blindness worldwide and mostly affects persons over the age of 60. Early-stage blindness may be reduced with timely and precise screening. High-resolution analysis and identification of the retinal layers damaged by illness is made possible by optical coherence tomography (OCT), a diagnostic technique. Setting up a comprehensive eye screening system to identify AMD is a difficult task. Manually sifting through OCT pictures for anomalies is a time-consuming and error-prone operation. Automatic feature extraction from OCT images may speed up the diagnostic process and reduce the potential for human mistake. Historically, several methods have been developed to identify characteristics in OCT pictures. This thesis documents the development and evaluation of many such algorithms for the identification of AMD. In order to minimize the severity of AMD, retinal fundus images must be employed for early detection and classification. In this work, we develop a useful deep learning cloud-based AMD categorization model for wearables. The suggested model is DLCTO-AMDC model, a patient outfitted with a head-mounted camera (OphthoAI IoMT headset) may send retinaldehyde fundus imageries to a secure virtual server for analysis. The suggested AMD classification model employs Inception v3 as the feature extractor and a noise reduction approach based on midway point filtering (MPF). The deep belief network (DBN) model is also used to detect and classify AMD. Then, an AOA-inspired hyperparameter optimisation method is used to fine-tune the DBN parameters. To ensure the DLCTO-AMDC model would provide superior classification results, extensive simulations were done using the benchmark dataset. The findings prove the DLCTO-AMDC model is superior to other approaches already in use.

Faster region-based convolutional neural network for plant-parasitic and non-parasitic nematode detection

Nematodes represent very abundant and the largest species diversity in the world. Nematodes, which live in a soil environment, possess several functions in agricultural systems. There are two huge groups of soil nematodes, a non-parasitic nematode, which contributes positively to ecological processes, and a plant-parasitic nematode, which cause various disease and reduces yield losses in the agricultural system. Early detection and classification in the agricultural area infected with plant-parasitic nematode and interpreting the soil level condition in this area required a fast and reliable detection system. However, nematode identification is challenging and time-consuming due to their similar morphology. This study applied a pre-trained faster region-based convolutional neural network (RCNN) for plant-parasitic and non-parasitic nematodes detection. These deep learning-based object detection models gave satisfactory results as the accuracy reached 87.5%.

Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.

ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems.

Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the performance of most traditional machine learning (ML) classifiers is questionable, as the interrelationship between the learning parameters is not well modeled, especially for data features with high dimensions. To address the limitations of ML classifiers, this paper introduces an automatic arrhythmia classification approach based on the integration of a recent metaheuristic optimization (MHO) algorithm and ML classifiers. The role of the MHO is to optimize the search parameters of the classifiers. The approach consists of three steps: the preprocessing of the ECG signal, the extraction of the features, and the classification. The learning parameters of four supervised ML classifiers were utilized for the classification task; support vector machine (SVM), k-nearest neighbors (kNNs), gradient boosting decision tree (GBDT), and random forest (RF) were optimized using the MHO algorithm. To validate the advantage of the proposed approach, several experiments were conducted on three common databases, including the Massachusetts Institute of Technology (MIT-BIH), the European Society of Cardiology ST-T (EDB), and the St. Petersburg Institute of Cardiological Techniques 12-lead Arrhythmia (INCART). The obtained results showed that the performance of all the tested classifiers were significantly improved after integrating the MHO algorithm, with the average ECG arrhythmia classification accuracy reaching 99.92% and a sensitivity of 99.81%, outperforming the state-of the-art methods.

Advanced imaging system for brain tumor automatic classification from MRI images using HOG and BOF feature extraction approaches

AbstractBrain tumors have recently become a widespread phenomenon that affects almost all age ranges. Magnetic resonance imaging (MRI) is a widely used technique nowadays to diagnose this life‐threatening disease. Therefore, using a computer‐aided diagnosis system is essential for the early identification and classification of brain tumors. This work aims to develop a fully automated classification framework for MRI brain tumor images using machine learning (ML) algorithms. In this article, four ML algorithms such as support vector machines (Cubic SVM), k‐nearest neighbors (fine KNN), decision tree (fine tree), and naive Bayes (Kernel) were implemented with two feature extraction approaches: histogram of oriented gradients (HOG) and a bag of features (BOF) approach. The novelty of this work is based on the original usage of BOF and HOG approaches over different types of ML algorithms for MRI brain tumor image classification. In addition, combining two public datasets that contain three brain tumor types to generalize and improve the proposed work. The results showed that the fine KNN algorithm achieved the greatest success among all algorithms using the HOG method with an accuracy of 98.62% on 6328 MRI images.