Detection Of Disease Research Articles

Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.

Handheld echocardiography for the screening and diagnosis of rheumatic heart disease: a systematic review to inform WHO guidelines

Early detection and diagnosis of acute rheumatic fever and rheumatic heart disease are key to preventing progression, and echocardiography has an important diagnostic role. Standard echocardiography might not be feasible in high-prevalence regions due to its high cost, complexity, and time requirement. Handheld echocardiography might be an easy-to-use, low-cost alternative, but its performance in screening for and diagnosing acute rheumatic fever and rheumatic heart disease needs further investigation. In this systematic review and meta-analysis, we searched Embase, MEDLINE, LILACS, and Conference Proceedings Citation Index-Science up to Feb 9, 2024, for studies on the screening and diagnosis of acute rheumatic fever and rheumatic heart disease using handheld echocardiography (index test) or standard echocardiography or auscultation (reference tests) in high-prevalence areas. We included all studies with useable data in which the diagnostic performance of the index test was assessed against a reference test. Data on test accuracy in diagnosing rheumatic heart disease, acute rheumatic fever, or carditis with acute rheumatic fever (primary outcomes) were extracted from published articles or calculated, with authors contacted as necessary. Quality of evidence was appraised using GRADE and QUADAS-2 criteria. We summarised diagnostic accuracy statistics (including sensitivity and specificity) and estimated 95% CIs using a bivariate random-effects model (or univariate random-effects models for analyses including three or fewer studies). Area under the curve (AUC) was calculated from summary receiver operating characteristic curves. Heterogeneity was assessed by visual inspection of plots. This study was registered with PROSPERO (CRD42022344081). Out of 4868 records we identified 11 studies, and two additional reports, comprising 15 578 unique participants. Pooled data showed that handheld echocardiography had high sensitivity (0·87 [95% CI 0·76-0·93]), specificity (0·98 [0·71-1·00]), and overall high accuracy (AUC 0·94 [0·84-1·00]) for diagnosing rheumatic heart disease when compared with standard echocardiography (two studies; moderate certainty of evidence), with better performance for diagnosing definite compared with borderline rheumatic heart disease. High sensitivity (0·79 [0·73-0·84]), specificity (0·85 [0·80-0·89]), and overall accuracy (AUC 0·90 [0·85-0·94]) for screening rheumatic heart disease was observed when pooling data of handheld echocardiography versus standard echocardiography (seven studies; high certainty of evidence). Most studies had a low risk of bias overall. Some heterogeneity was observed for sensitivity and specificity across studies, possibly driven by differences in the prevalence and severity of rheumatic heart disease, and level of training or expertise of non-expert operators. Handheld echocardiography has a high accuracy and diagnostic performance when compared with standard echocardiography for diagnosing and screening of rheumatic heart disease in high-prevalence areas. World Health Organization. For the Chinese, French, Italian, Persian, Portuguese, Spanish and Urdu translations of the abstract see Supplementary Materials section.

Detection of chronic kidney disease using binary whale optimization algorithm

Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.

Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration

<span lang="EN-US">Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. </span>

HMGD: A High-Accuracy Model for Detection and Prediction of Respiratory Genetic Diseases

HMGD: A High-Accuracy Model for Detection and Prediction of Respiratory Genetic Diseases

Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.

Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

Hardware and software co-design for detecting hypertension from photoplethysmogram

Hypertension is one of the leading causes of cardiovascular disease morbidity in the world. If remains untreated, it may cause severe damage like heart attack or even death. Early detection is required to prevent the development of other cardiac abnormalities. Photoplethysmogram (PPG) is a bio signal that can be obtained optically by a sensor. It is studied to monitor the change of volume of blood and detect heart conditions. Previous studies have already applied PPG to detect hypertension at the software level. In this article, along with software-based detection, we have implemented a digital hardware-based system for detecting hypertension from signals recorded using PPG sensor. Xilinx ZedBoard Zynq-7000 field programmable gate array (FPGA) board is utilized for designing the embedded system. The hypertension detection accuracy is 98.02% at the software level while for the digital system, it is 96.05% consuming 0.374 W power. The study can be analyzed for other cardiac disease detection and medical equipment development.

Advancing machine learning for identifying cardiovascular disease via granular computing

<p>Machine learning in cardiovascular disease has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for cardiovascular disease identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, K-Nearest Neighbor (KNN), Random Forest, and Gradient Boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in cardiovascular disease detection.</p>

Crop Disease Prediction Using Web Application

Abstract: Agriculture plays a very vital role in our life. Without agriculture, the existence of human beings is not possible as it is the main source of our food supply to sustain on the earth and it also helps to grow our economy across the world. Plant disease detection is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector. Various diseases like Common Rust, Bacterial Spot, Leaf Mold, Mosaic Virus, Powdery Mildew and others that could affect a plant and cause farmer to lose a substantial sum yearly. Deep Learning (DL) can play a crucial role in helping farmers to prevent crop failure by early disease detection in plant leaves. In the experiment, examination conducted by Convolution Neural Network (CNN) model on dataset (which consists of images of healthy and infected leaves) to detect plant diseases and Web Application is used for real-life crop disease prediction. The proposed Web Application aims to assist farmers in detection of plant diseases by analyzing images of the plant leaves. The proposed application uses the CNN model to distinguish healthy and infected leaves. The goal is to help farmers and prevent economic loss by detecting plant diseases early

Multiple Disease Detection System Using Machine Learning

Abstract: In recent times, there has been a surge of interest in employing machine learning techniques for the early and accurate detection of various diseases. This research introduces a holistic approach to constructing a robust multi-disease detection system that utilizes advanced machine learning algorithms. Our proposed system integrates diverse datasets encompassing a range of medical conditions, enabling the simultaneous detection of multiple diseases within a unified framework. We leverage cutting-edge machine learning models, including, but not limited to, [specify the models used], to analyze and interpret intricate patterns within the data.

Leaf Disease Detection Using Convolutional Neural Network

Leaf Disease Detection Using Convolutional Neural Network

Comparative Analysis of VGG16, RESNET50, AND CNN Models for Lung Disease Prediction: A Deep Learning Approach

Abstract: Utilizing deep learning methods in medical image analysis has shown promise in enhancing disease detection and diagnosis. In this study, we conducted a detailed comparative analysis of threewidely recognized convolutional neural network (CNN) architectures: VGG16, ResNet50, and a custom CNN model tailored for pneumonia and COVID-19 detection from chest X-ray images. Leveraging transfer learning techniques and meticulously curated datasets, we evaluated the models' performance in accurately identifying respiratory diseases. Our investigation utilized two publicly available datasets, the ChestX-ray14 dataset and the NIH Chest X-ray Dataset, both annotated for pneumonia and COVID-19. Prior to model training, we conducted thorough preprocessing to ensure optimal data quality and consistency. Through rigorous experimentation, we assessed the models' accuracy, sensitivity, and specificity in disease detection. The results revealed nuanced differences in model performance across disease categories. While VGG16 demonstrated robust accuracy in pneumonia detection, ResNet50 exhibited enhanced sensitivity and specificity in identifying COVID-19 cases. Our custom CNN model, leveraging insights from both architectures, showcased competitive performance, emphasizing the importance of tailored model design for optimal diagnostic outcomes. Through comprehensive analysis and discussion, we elucidated the strengths and limitations of each model, considering factors such as computational efficiency, interpretability, and generalizability. Our findings underscore the potential of deep learningbased diagnostic tools in supporting healthcare professionals in timely and accurate disease diagnosis

Deep Learning Approach for Early Detection of Alzheimers Disease

Abstract: Alzheimer’s disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer’s disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages.

Med-Tech-AI: Exploring Artificial Intelligence for Enhanced Healthcare Research

Abstract: This paper explores the potential of Artificial Intelligence (AI) to revolutionize healthcare research through the MedTech-AI project. The project investigates the development and evaluation of AI models for medical image analysis, aiming for improved disease detection and classification. It examines the potential of AI-powered clinical decision support systems (CDSS) to assist healthcare professionals. Additionally, the project explores Natural Language Processing (NLP) techniques for extracting valuable insights from unstructured healthcare data like electronic health records and medical literature. Finally, the paper investigates the use of AI for predictive analytics in disease prevention, identifying risk factors and informing preventative measures. By examining these areas, the Med-Tech-AI project establishes a foundation for understanding how AI can enhance healthcare research, potentially leading to improved healthcare efficiency, accuracy, and ultimately, better patient outcomes

A Review: CNN Based Plant Disease Detection

Abstract: Identifying plant diseases is the initial step toward mitigating losses in agricultural product yield and quantity. This work focuses on exploiting such technical breakthroughs, with an actual focus on plant disease detection. The system will analyze plant photos using cutting-edge image processing algorithms to precisely diagnose diseases, gauge the extent of damage, and make wise suggestions for necessary pesticides and nutrient supplements. The suggested remedy will identify particular nutrient deficits causing problems with crop health in addition to detecting the existence of illnesses. The system will aid farmers in making well-informed decisions about how to use nutrients and pesticides by integrating a thorough analysis. This will ultimately help to manage crop diseases in a timely and efficient manner, maximize agricultural yield, and reduce financial losses

Pioneering Dermatological Disease Detection with CNNs

Abstract: Skin cancer poses a significant public health concern globally, with its incidence steadily rising. Early and accurate detection plays a pivotal role in improving patient outcomes and survival rates. With leveraging advancements in deep learning, this project explores the efficacy of Convolutional Neural Networks (CNNs) in automating the classification and detection of skin cancer from dermatoscopic images. The methodology employed in this project involves the development and optimization of a CNN architecture tailored for skin cancer analysis. The proposed model harnesses the hierarchical feature learning capabilities of CNN to automatically extract intricate patterns and discriminative features from dermatoscopic images, enabling accurate differentiation among various skin lesion types, including melanoma, basal cell carcinoma, and benign nevi. In addition to classification, the developed CNN architecture integrates a lesion detection mechanism. This auxiliary functionality enables the identification and delineation of potentially cancerous regions within skin lesions. The proposed model’s detection capabilities are evaluated through bounding box prediction and segmentation masks, exhibit notable performance in localizing suspicious areas, aiding dermatologists in targeted examinations. With the help of this project, we can prevent the cause of damage to skin at an early stage.

Parkinson Disease Detection Using CNN Algorithm

Abstract: Parkinson's disease, a progressive neurological disorder, results from the depletion of dopamine-producing neurons in the brain, leading to diminished motor function. Common symptoms include tremors, rigidity, bradykinesia, shivering, and impaired balance. This study introduces two neural network architectures: the Voice Impairment Classifier, designed for early disease detection. The research conducted a thorough assessment of convolutional neural networks (CNNs) for classifying gait signals transformed into spectrogram images, and deep dense networks for analyzing voice recordings. Results demonstrated superior performance of the proposed models, with the VGFR Spectrogram Detector achieving 88.1% accuracy and the Voice Impairment Classifier achieving 89.15% accuracy, surpassing current state-of-the-art techniques.

Adult niemann-pick disease: A case report

Adult niemann-pick disease: A case report

Intelligent crop management system for improving yield in maize production: evidence from India

PurposeManaging weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and healthDesign/methodology/approachWe propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presentedFindingsWeed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actionsPractical implicationsThe proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize cropSocial implicationsThe proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.Originality/valueThis study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature