Techniques For Detection Of Disease Research Articles

High-resolution superlet transform based techniques for Parkinson's disease detection using speech signal

Parkinson's Disease (PD) is a matter of great concern when it comes to the health management of elderly people. Tremors, muscle stiffness, change in cognitive abilities, and dysarthria are some of the frequently encountered symptoms, commonly found in patients diagnosed with PD. A substantive progression of PD can be seen in the patient's body, caused by the degeneration of the dopamine-generating cells. PD is still a non-curable disease but its progression can be slowed down with some medications and therapies. Thus, early detection of the disease becomes a necessity for much-needed care of the patients. Recent developments in the technology sector have rewarded us with an opportunity for early detection of PD using speech signals. In this paper, a Deep Neural Network (DNN) based approach using spectrograms of speech signals generated by Superlet Transform (SLT) is proposed for the detection of PD. The SLT converts the 1-D speech signals into 2-D spectrogram. These spectrograms of the speech signal are then applied to different DNN classifiers namely InceptionResNetV2, VGG-16, and ResNet50v2 for PD detection. The proposed method is evaluated on sustainable vowels, modulated vowels, DDK analysis, and isolated words of PC-GITA dataset and vowels of ItalianPVS dataset. Several evaluation measures have been used to estimate the robustness and explainability of this work. The developed framework gives the best performance on modulated vowels using VGG-16 on PC-GITA dataset. The VGG-16 with SLT achieved an overall accuracy of 92% with sensitivity, specificity, precision, and F-1 score of 92%, 91%, 95%, and 93%, respectively. The proposed method achieved an accuracy of 96% on the ItalianPVS dataset. This method outperforms the state-of-the-art methods like Hilbert spectrum analysis, Empirical Mode Decomposition (EMD), Continuous Wavelet Transform (CWT), and Short-Time Fourier Transform (STFT) for PD detection using different speech data sources.

Recent advances of application of optical imaging techniques for disease detection in fruits and vegetables: A review

Fruits and vegetables are among the agricultural products that enjoy a high demand in the market. However, the most critical challenge in the production of fruits and vegetables is disease infections which can lead to economic loss. The current assessment method of disease infection still relies on conventional methods and laboratory analysis. The conventional methods although simple and easy are laborious and have less accuracy while laboratory analysis is costly and time-consuming. Over the last two decades, optical imaging techniques have emerged and are gaining interest in the agricultural and food industries. These techniques offer easy, rapid, accurate, reliable as well as user-friendly sensing tools for the identification of quality and disease infection in agricultural products. The fundamental aspect in the techniques is the interaction of light with the tissue and the manipulation of light when interacting with a surface. The integration of edge computing along with artificial intelligence has also reshaped the technology to another level. The use of other agricultural technologies such as drones or robots enables real-time monitoring and efficient farm management. Thus, this paper reviews recent advances in the application of optical imaging techniques for disease detection in fruits and vegetables. This includes computer vision, multispectral imaging, hyperspectral imaging, biospeckle and thermal imaging. The feasibility of the optical imaging techniques is discussed. Challenges and considerations for future research are also highlighted. This review provides a new insight into the recent application of optical imaging which is not only beneficial for the agricultural and food industries but also to other relevant industries.

Citrus Fruit Disease Detection Techniques: A Survey and Comparative Analysis of Relevant Approaches

Citrus Fruit Disease Detection Techniques: A Survey and Comparative Analysis of Relevant Approaches

Advancing Sugarcane Disease Detection through CNN-Based Deep Learning

Agriculture produce especially sugarcane crop is no exception to diseases as compared to the other crops. Sugarcane, a vital cash crop for the global sugar industry, faces numerous challenges, with the Top Borer disease. Disease prone sugarcane crop directly affects the production quality and quantity. Sugarcane infections are a cause of worry for the farmers because they can wipe out the entire crop field. Researchers are working on applying Artificial Intelligence (AI) techniques, like Machine Learning (ML) and Deep Learning (DL), to analyse the agricultural data (yield prediction, selling price forecasting, climate, and soil quality etc.) and prevent crop damage due to various reasons, diseases being one of them. Deep neural network which includes Convolutional Neural Network (CNN) is a modern technique for agricultural disease detection. Hence, this paper presents the feasibility study and the effectiveness of DL based CNN algorithm in the disease detection of crops with special reference to selective four diseases of sugarcane crop in India. The proposed system integrates state-of-the-art deep learning algorithms, leveraging Convolutional Neural Networks (CNNs) and recurrent models, to analyze high-resolution images captured by unmanned aerial vehicles (UAVs) or ground-based sensors. These images provide a comprehensive view of the sugarcane plantation, allowing for the identification of subtle symptoms and early-stage infections that may go unnoticed by the human eye. The key components of the developed system include a robust image preprocessing pipeline to enhance the quality of input data, a customized deep neural network architecture trained on a diverse dataset of sugarcane images, and a real-time monitoring system for timely intervention. The model's performance is evaluated on a large-scale dataset collected from sugarcane plantations across diverse geographic regions. The results demonstrate the system's high accuracy in detecting and classifying the Top Borer disease, outperforming traditional methods.

Evaluating the Effectiveness of Machine Learning and Computer Vision Techniques for the Early Detection of Maize Plant Disease

Monitoring plant growth is a crucial agricultural duty. In addition, the prevention of plant diseases is an essential component of the agricultural infrastructure. This technique must be automated to keep up with the rising food demand caused by increasing population expansion. This work evaluates this business, specifically the production of maize, which is a significant source of food worldwide. Ensure that Mazie's yields are not damaged is a crucial endeavour. Diseases affecting maize plants, such as Common Rust and Blight, are a significant production deterrent. To reduce waste and boost production and disease detection efficiencies, the automation of disease detection is a crucial strategy for the agricultural sector. The optimal solution is a self-diagnosing system that employs machine learning and computer vision to distinguish between damaged and healthy plants. The workflow for machine learning consists of data collection, data preprocessing, model selection, model training and testing, and evaluation.

An Approach to Binary Classification of Alzheimer's Disease Using LSTM.

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

Machine learning techniques for plant disease detection: an evaluation with a customized dataset

<p>Diseases in edible and industrial plants remains a major concern, affecting producers and consumers. The problem is further exacerbated as there are different species of plants with a wide variety of diseases that reduce the effectiveness of certain pesticides while increasing our risk of illness. A timely, accurate and automated detection of diseases can be beneficial. Our work focuses on evaluating deep learning (DL) approaches using transfer learning to automatically detect diseases in plants. To enhance the capabilities of our approach, we compiled a novel image dataset containing 87,570 records encompassing 32 different plants and 74 types of diseases. The dataset consists of leaf images from both laboratory setups and cultivation fields, making it more representative. To the best of our knowledge, no such datasets have been used for DL models. Four pre[1]trained computer vision models, namely VGG-16, VGG-19, ResNet-50, and ResNet-101 were evaluated on our dataset. Our experiments demonstrate that both VGG-16 and VGG-19 models proved more efficient, yielding an accuracy of approximately 86% and a f1-score of 87%, as compared to ResNet-50 and ResNet-101. ResNet-50 attains an accuracy and a f1-score of 46.9% and 45.6%, respectively, while ResNet-101 reaches an accuracy of 40.7% and a f1-score of 26.9%.</p>

Clinical profiles and outcomes of patients enrolled in the department of health: Breast cancer medicines access program in the philippines—A single center experience.

6 Background: The magnitude of breast cancer as a major public health problem throughout the world has always been emphasized. In the Philippines, the incidence of breast cancer is 17.7% in 2020, which makes it the most common malignancy among women. According to the American Cancer Society, when breast cancer is detected early, the 5-year relative survival rate is 99% as compared to a patient with distant metastasis, which decreased to 29%. Therefore, enhanced screening techniques for early disease detection and prompt treatment are necessary to improve cure and survival rates. The Breast Cancer Medicines Access Program (BCMAP) aimed to provide ready access to free medicines for the adjuvant chemotherapy treatment of Stage I-IIIb breast cancer patients to alleviate financial toxicity associated with the costly cancer treatment. This study aims to assess the impact of the BCMAP, and determine the clinical profiles and outcomes of patients enrolled in the program at National Kidney and Transplant Institute. Specifically, this study aims to identify factors causing delay of treatment, to evaluate the program and to provide recommendations for implementation improvement. Methods: This is a retrospective cohort study which included all enrolled patients at the BCMAP from October 2020 to December 2021. The patients were followed up until July 2022 to determine survival rates. Results: There were 52 eligible breast cancer patients enrolled. More than two-thirds completed systemic treatment as per physician’s discretion. The results showed that the overall survival rate is at 95.9% and the disease-free survival rate is at 88.7%. Less than 10% had progressed, and there were two mortalities reported. COVID-19 infection (p=0.0001) and unavailability of trastuzumab (p=0.0216) were identified to be significant factors for treatment delay. Conclusions: There was a trend towards a favorable overall survival and disease-free survival for patients with early breast cancer enrolled to DOH-BCMAP at 21 months follow-up. COVID-19 infection and unavailability of Trastuzumab were significant factors for treatment delay.

Impact of cross border reverse migration in Delhi–UP region of India during COVID-19 lockdown

Abstract The declaration of a nationwide lockdown in India led to millions of migrant workers, particularly from Uttar Pradesh (UP) and Bihar, returning to their home states without proper transportation and social distancing from cities such as Delhi, Mumbai, and Hyderabad. This unforeseen migration and social mixing accelerated the transmission of diseases across the country. To analyze the impact of reverse migration on disease progression, we have developed a disease transmission model for the neighboring Indian states of Delhi and UP. The model’s essential mathematical properties, including positivity, boundedness, equilibrium points (EPs), and their linear stability, as well as computation of the basic reproduction number ( R 0 ) \left({R}_{0}) , are studied. The mathematical analysis reveals that the model with active reverse migration cannot reach a disease-free equilibrium, indicating that the failure of restrictive mobility intervention caused by reverse migration kept the disease propagation alive. Further, PRCC analysis highlights the need for effective home isolation, better disease detection techniques, and medical interventions to curb the spread. The study estimates a significantly shorter doubling time for exponential growth of the disease in both regions. In addition, the occurrence of synchronous patterns between epidemic trajectories of the Delhi and UP regions accentuates the severe implications of migrant plight on UP’s already fragile rural health infrastructure. By using COVID-19 incidence data, we quantify key epidemiological parameters, and our scenario analyses demonstrate how different lockdown plans might have impacted disease prevalence. Based on our observations, the transmission rate has the most significant impact on COVID-19 cases. This case study exemplifies the importance of carefully considering these issues before implementing lockdowns and social isolation throughout the country to combat future outbreaks.

A systematic survey on deep learning techniques for chest disease detection using chest radiographs

The lung is one of the most sensitive internal organs of the human body that gets infected by constant exposure to chemicals, particles, and infectious organisms in the atmospheric air. Due to this, the most dangerous chest diseases take place which are the leading cause of human disability and death throughout the world. These pulmonary diseases can be recognized by medical imaging techniques i.e. Chest radiography, Computed tomography, Pulmonary and bronchial angiography, Magnetic resonance imaging, Ultrasonography, and Nuclear medicine techniques. A massive amount of chest reports are generated every day that contain a large amount of anatomical and potentially pathological information, but manual detection and classification of chest abnormalities are considered a tedious and time-taking task. In addition, it also requires skilled radiologists as these reports are often difficult to read and differentiate the pathologies of the chest. To eradicate this issue and give a value-added solution; artificial intelligence and deep learning-based algorithms show excellent performance and have proven their effectiveness for object recognition and image segmentation. The primary aim of this study is to present a comprehensive analysis of the deep learning approaches used to identify various types of pulmonary pathologies on CXRs. In addition, we provide a detailed analysis of the most popular open-access CXR datasets, taxonomy of the state-of-the-art works to assist the researchers in preparing the plan for their research contribution, and discuss potential future research directions in this field. More than 350 research papers from various indexing services, including Web of Science, Scopus, PubMed, and IEEE, were considered for this review article. After several selection parameters, it is observed that most of the literature focuses on the DL approach with CXRs. However, few publications have focused on CT scans and Ultrasound images.

Refined single-stage object detection deep-learning technique for chilli leaf disease detection

Refined single-stage object detection deep-learning technique for chilli leaf disease detection

Isolation and Characterization of Bacteria Associated with Onion and First Report of Onion Diseases Caused by Five Bacterial Pathogens in Texas, U.S.A.

Bacterial diseases pose a severe challenge to growers and cause significant loss to the billion-dollar onion industry in the United States. Texas is the sixth largest onion producing state, yet the bacterial communities associated with short-day onion crops grown in Texas have not been studied. This study was conducted to identify, characterize, and understand the diversity of bacteria associated with onion production in Texas. In 2020, 190 foliar and 210 bulb samples were collected from onion crops in the Rio Grande Valley and Winter Garden regions of Texas. Sequencing of the 16s rRNA gene was used to identify each bacterial strains to a genus. The pathogenicity to onion of each bacterial strain was tested using three assays: a red onion scale assay, a yellow onion bulb assay, and a foliar assay. Whole genome sequencing was done to identify the onion-pathogenic strains to species. Collectively, isolates of 24 genera belonging to three phyla were detected, including 19 genera from foliar samples and nine genera from bulb samples. Isolates in the Phylum Proteobacteria, including 15 genera of Gram-negative bacteria, were the most abundant of the taxa, comprising 90.0% of the strains isolated. The diversity of foliar isolates was evenly distributed between Gram-positive and Gram-negative bacteria, while Gram-negative bacteria dominated the isolates from bulb samples. In total, 83.9% of the bacterial isolates were not pathogenic on onion, with only isolates of Pantoea, Pseudomonas, Burkholderia, Erwinia, Enterobacter, and Curtobacterium proving pathogenic. Strains of Burkholderia gladioli, Pseudomonas alliivorans, Pantoea agglomerans, P. ananatis, and P. allii are the first documented cases of these pathogens of onion in Texas. Identifying and characterizing the nature of onion microflora, including pathogens of onion, is vital to developing rapid disease detection techniques via pathogenomics and minimizing losses through the application of effective disease management measures.

Study on segmentation and prediction of lung cancer based on machine learning approaches

Lung cancer is a dangerous disease in human health. At the early stage, lung cancer detection provides a way to save human life. As a result, improvements in Deep Learning (DL), a technique, a branch of Machine Learning (ML), have helped to identify and classify lung cancer in clinical photographs. DL technology has also outperformed traditional methods in a variety of fields. Researchers are exploring various DL techniques for disease detection to improve the accuracy of the CAD systems in CT lung cancer detection. In this experiment, cutting-edge ML and DL methods for lung disease have been recommended as CAD systems after thoroughly analysing existing frameworks. It can be separated into FP reduction systems and system to detect nodule. The primary characteristics of various approaches are analyzed. The CT lung datasets existing for examination and evaluation with the various approaches are also presented and discussed.

Cloud Computing with Artificial Intelligence Techniques for Effective Disease Detection

Cloud Computing with Artificial Intelligence Techniques for Effective Disease Detection

Cloud Computing with Artificial Intelligence Techniques for Effective Disease Detection

With the current rapid advancement of cloud computing (CC) technology, which enabled the connectivity of many intelligent objects and detectors and created smooth data interchange between systems, there is now a strict need for platforms for data processing, the Internet of Things (IoT), and data management. The field of medicine in CC is receiving a lot of attention from the scientific world, as well as the private and governmental sectors. Thousands of individuals now have a digital system due to these apps where they may regularly obtain helpful medical advice for leading a healthy life. The use of artificial intelligence (AI) in the medical field has several advantages, including the ability to automate processes and analyze large patient databases to offer superior medicine more quickly and effectively. IoT-enabled smart health tools provide both internet solutions and a variety of features. CC infrastructure improves these healthcare solutions by enabling safe storage and accessibility. We suggest a novel Cloud computing and artificial intelligence (CC-AI) premised smart medical solution for surveillance and detecting major illnesses to provide superior solutions to the users. For disease detection, we suggested AI-based whale optimization (WO) and fuzzy neural network (FNN) (WO-FNN). Patients' IoT wearable sensor data is gathered for detection. The accuracy, sensitivity, specificity, and computation time are evaluated and compared with existing techniques.

Automatic Recognition of Rice Leaf Diseases Using Transfer Learning

Rice, the world’s most extensively cultivated cereal crop, serves as a staple food and energy source for over half of the global population. A variety of abiotic and biotic factors such as weather conditions, soil quality, temperature, insects, pathogens, and viruses can greatly impact the quantity and quality of rice grains. Studies have established that plant infections have a significant impact on rice crops, resulting in substantial financial losses in agriculture. To accurately diagnose and manage the diseases affecting rice plants, plant pathologists are seeking efficient and reliable methods. Traditional disease detection techniques, employed by farmers, involve time-consuming visual inspections and result in inadequate farming practices. With advancements in agricultural technology, the identification of pathogenic organisms in rice plants has become significantly more manageable through techniques such as machine learning and deep learning, which are receiving significant attention in crop disease research. In this paper, we used the transfer learning approach on 15 pre-trained CNN models for the automatic identification of Rice leave diseases. Results showed that the InceptionV3 model is outperforming with an average accuracy of 99.64% with Precision, Recall, F1-Score, and Specificity as 98.23, 98.21, 98.20, and 99.80, and the AlexNet model resulted in poor performance with average accuracy of 97.35% among others.

Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing

In recent decades, deep-learning dependent fruit disease detection and classification techniques have evinced outstanding results in technologically advanced horticulture investigation. Due to the comparatively limited image processing capabilities of edge computing devices, implementing deep learning methods in actual field scenarios is currently difficult. The use of intelligent machines in contemporary horticulture is being hampered by these restrictions, which are emerging as a new barrier. In this research, we present an efficient model for citrus fruit disease prediction. The proposed model utilizes the fusion of deep learning models CNN and LSTM with edge computing. The proposed model employs an enhanced feature-extraction mechanism, with a down-sampling approach, and then a feature-fusion subsystem to ensure significant recognition on edge computing devices with retaining citrus fruit disease detection accuracy. This research utilizes the online Kaggle and plan village dataset which contains 2950 citrus fruit images with disease categories black spots, cankers, scabs, Melanosis, and greening. The proposed model and existing model are tested with two features with pruning and without pruning and compared based on various performance measuring parameters, i.e., precision, recall, f-measure, and support. In the first phase experimental analysis is performed using Magnitude Based Pruning and in the second phase Magnitude Based Pruning with Post Quantization. The proposed CNN-LSTM model achieves an accuracy rate of 97.18% with Magnitude-Based Pruning and 98.25% with Magnitude-Based Pruning with Post Quantization, which is better as compared to the existing CNN method.

Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review

Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review

Multivariate Vocal Data Analysis with Deep Learning Applied for Parkinson Disease Detection

Different decision-making skills are used differently in the advancement of ML and DL techniques. In particular, it will probably be necessary to use ML and DL approaches for disease detection. The proposed effort uses acoustic-based DL approaches' ability to detect PD symptoms. This investigation also examines a number of motor metrics such as nonmotor PD measurements in this regard. Many DL approaches, including deep knowledge generation networks and recurrent networks, can be employed to identify this disease. Different decision-making capabilities are used differentially in the deployment of ML and DL methods. In particular, it will soon be necessary to use Machine learning and Deep Learning techniques for disease detection. The proposed effort uses acoustic-based DL approaches' ability to detect PD symptoms. This investigation also examines a number of motor metrics such as nonmotor PD measurements in this regard. Many DL approaches, including deep knowledge generation networks and recurrent networks, can be employed to identify this disease.

Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng (Panax ginseng Meyer).

Root rot of Panax ginseng caused by Cylindrocarpon destructans, a soil-borne fungus is typically diagnosed by frequently checking the ginseng plants or by evaluating soil pathogens in a farm, which is a time- and cost-intensive process. Because this disease causes huge economic losses to ginseng farmers, it is important to develop reliable and non-destructive techniques for early disease detection. In this study, we developed a non-destructive method for the early detection of root rot. For this, we used crop phenotyping and analyzed biochemical information collected using the HSI technique. Soil infected with root rot was divided into sterilized and infected groups and seeded with 1-year-old ginseng plants. HSI data were collected four times during weeks 7-10 after sowing. The spectral data were analyzed and the main wavelengths were extracted using partial least squares discriminant analysis. The average model accuracy was 84% in the visible/near-infrared region (29 main wavelengths) and 95% in the short-wave infrared (19 main wavelengths). These results indicated that root rot caused a decrease in nutrient absorption, leading to a decline in photosynthetic activity and the levels of carotenoids, starch, and sucrose. Wavelengths related to phenolic compounds can also be utilized for the early prediction of root rot. The technique presented in this study can be used for the early and timely detection of root rot in ginseng in a non-destructive manner.