Approach For Disease Detection Research Articles

A comprehensive review on deep cardiovascular disease detection approaches: its datasets, image modalities and methods

A comprehensive review on deep cardiovascular disease detection approaches: its datasets, image modalities and methods

Image-Based Face Recognition Techniques Used in Disease Detection Approaches: A Survey

Facial diseases lead to noticeable changes on the human face, and some of them extend beyond internal effects or organ-based disorders. Indeed, certain types of facial diseases result in visually noticeable abnormalities on the human face. These alterations in facial patterns can serve as potential indicators for corresponding diseases, particularly in the fields of endocrinology and metabolism, Muscles-Nervous disorders, Chromosomes, and Genetic disorders, among others. Technologies used in Face Recognition (FR) have been developed over the past few decades; however, only a limited amount of research has been applied in recent years to FR-based disease detection for clinical purposes. FR applications relying on Artificial Neural Network (ANN) techniques have recorded higher accuracy rates in diagnosing facial diseases. This field of recognition holds promising potential for optimizing facial diagnosis approaches and supporting medical staff in evaluating detections. In practice, only a few research ideas have been translated into medical products, emphasizing the need to identify and integrate future applications. As a primary focus, this paper centers on the key applications and technologies for detecting various types of facial diseases, along with a discussion of prospects.

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection.

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model's resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.

Review of the State of the Art of Transfer Learning for Plant Leaf Diseases Detection

Plant leaf diseases can have a significantly negative influence on the quantity and quality of agricultural cultivation, as well as the safety of food production. Plant leaf diseases could potentially entirely prevent the harvest of grains in some situations. Therefore, it is extremely important from a pragmatic standpoint to look for quick, automatic, cheap, and accurate ways to detect plant leaf diseases. One of the well-known plant leaf disease detection approaches is deep learning. Deep learning has several drawbacks as a result of the huge amount of data required to train the network. When a dataset has inadequate photographs, performance falls. An approach called "Transfer Learning" is an extensively used method for addressing the shortcomings of a small dataset, the length of the training process, and improving the performance of the model. In this study, we investigated transfer learning for deep CNNs to improve the learning capability to recognize leaf disease. This survey focuses on categorizing and analyzing the recent developments in transfer learning for Deep CNN situations to enhance learning performance by reducing the need for extensive training data collecting.

Grading of Pomegranate Fruit using CNN

Farmers require an automated system to grade Pomegranate fruits rather than a manual system to increase productivity and quality of Pomegranate fruits. Manual grading of fruits does not produce adequate results and requires additional time for disease identification and gradation, as well as the expertise of an expert, making it ineffective. The suggested system is an efficient module that identifies various pomegranate fruit disorders and determines the stage of sickness. Effective growth stage monitoring and disease detection are critical for maximising pomegranate fruit yield and quality. This paper describes a method for monitoring the growth stages of pomegranate fruit utilising image processing techniques and disease detection approaches based on machine learning algorithms. The suggested method analyses colour, shape, and texture information taken from photos captured at various phases of development to track the growth stages of pomegranate fruit. The obtained data is then utilised to train machine learning models that appropriately distinguish the growth stages. The models are trained on a dataset of annotated photos containing numerous pomegranate fruit illnesses. Farmers and agricultural specialists can use the developed technology to correctly monitor the growing phases of pomegranate fruit and detect problems. The camera in this project catches various pomegranate fruit stages and classifies pomegranate fruits into two classes: infected and non-infected, using a machine learning algorithm and Python tools. This study employs a CNN, K-mean method, and image processing technique to detect illnesses at various phases of fruit development.

Deep learning approach for disease detection in lumbosacral spine radiographs using ConvNet

ABSTRACT Lumbosacral exam enables several computer-aided diagnoses (CAD) applications since it provides detailed spine radiographs, specifically of the lumbar, sacral, and coccygeal regions. We developed a deep learning-based CAD system methodology to detect lumbosacral anomalies in this context. For this purpose, we used a heterogeneous dataset that contains frontal and lateral lumbosacral exams. Our classification experiments achieved promising results with pre-trained CNNs: accuracy of 0.82/0.86, kappa of 0.65/0.66, and F1-Score of 0.82/0.83 for frontal and lateral images, respectively. In addition, we developed an ensemble classification that combines frontal and lateral images of the same exam and uses a confidence threshold selection. In this ensemble, we managed to issue alerts of 22.31% with a False Discovery Rate of 2.79%. Our proposal allows specialists to assist in decision-making.

Emerging role of non-invasive and liquid biopsy biomarkers in pancreatic cancer

A global increase in the incidence of pancreatic cancer (PanCa) presents a major concern and health burden. The traditional tissue-based diagnostic techniques provided a major way forward for molecular diagnostics; however, they face limitations based on diagnosis-associated difficulties and concerns surrounding tissue availability in the clinical setting. Late disease development with asymptomatic behavior is a drawback in the case of existing diagnostic procedures. The capability of cell free markers in discriminating PanCa from autoimmune pancreatitis and chronic pancreatitis along with other precancerous lesions can be a boon to clinicians. Early-stage diagnosis of PanCa can be achieved only if these biomarkers specifically discriminate the non-carcinogenic disease stage from malignancy with respect to tumor stages. In this review, we comprehensively described the non-invasive disease detection approaches and why these approaches are gaining popularity for their early-stage diagnostic capability and associated clinical feasibility.

DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection

Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer's performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management.

Plant Disease Detection and Cure Recommendation Using Machine Learning

Abstract: Infection discovery in plants is a significant task thathas to be done in agriculture. To recognize the diseases in leaves, a continuous observation of the plants is required. Program- based identification of diseases in plants makes it easier to detect and reduces human efforts and time-saving. The proposed algorithm distinguishing sickness in plants and classifying them more accurately as compared to existing. Traditional approach for disease detection and classification requires huge amount of time and effort. In the last few years, advancement in technology and researchers’ focus makes it impossible to obtain optimized solution for it. A convolutional neural network is a form of artificial neural network specifically intended to process pixel input and is used for image recognition

Super Resolved Maize Plant Leaves Disease Detection Using Optimal Generative Adversarial Network

Agriculture plays a vital role in the economy and crop disease causes huge financial losses every year. The losses can be reduced by detecting the disease accurately. The variation in light intensity and complex background of the agricultural field in detecting the maize leaves disease are the biggest challenges. An optimization algorithm, named Cat Swarm Political Optimizer Algorithm (CSPOA) has been developed in this research to detect the disease of a maize plant leaf. Our proposed algorithm is an integration of the Cat Swarm Optimization (CSO) and Political Optimizer (PO) algorithm. Anisotropic filtering performs pre-processing for removing noise and the Region of Interest (ROI) extraction for enhancing the image quality. The super resolution image is obtained from the Low Resolution (LR) images using kernel regression model. After obtaining the super resolution image, the salient map extraction has been carried out for representing the saliency. Finally, the maize plant leaves disease classification process is done using General Adversarial Network (GAN) for identifying the maize leaves disease. The training of GAN develops the CSPOA. On comparing with the existing maize plant leaves disease detection approaches, the developed CSPOA-based GAN performed with a maximum accuracy 0.9056, maximum sensitivity 0.9599, and the maximum specificity 0.9592, respectively.

RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

SummaryHeart disease detection through early‐stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm‐based deep residual network (SWCA‐based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimization algorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm‐based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimization algorithm, named SWCA. Furthermore, developed SWCA‐enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925.

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images.

The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.

A comprehensive review on detection of plant disease using machine learning and deep learning approaches

Agriculture plays a significant part in India due to their population growth and increased food demands. Hence, there is a need to enhance the yield of crop. One of these important effects on low crop yields is diseases caused by bacteria, fungi and viruses. This can be prevented and handled by means of applying plant disease detection approaches. Machine learning techniques will be employed in the process of disease identification on plants as it mostly applies information themselves and offers fabulous techniques for detection of plant diseases. Methods based on Machine learning can be employed for the identification of diseases because it mainly applies on data superiority outcomes for specified task. In this approach, a comprehensive review has been made on the various techniques employed in plant disease detection using artificial intelligence (AI) based machine learning and deep learning techniques. Likewise, deep learning has also gained a great deal of significance in offering better performance outcome for detecting plant disease in the computer vision field. The deep learning advancements were employed to a range of domains that leads to great attainment in the machine learning and computer vision areas. The comparative study is made in terms of machine and deep learning techniques and their performance and usage in various research papers is related to show the effectiveness of deep learning model over machine learning model. In order to prevent major crop losses, the deep learning technique can be used to detect the leaf diseases from captured images.

Olive Disease Classification Based on Vision Transformer and CNN Models.

It has been noted that disease detection approaches based on deep learning are becoming increasingly important in artificial intelligence-based research in the field of agriculture. Studies conducted in this area are not at the level that is desirable due to the diversity of plant species and the regional characteristics of many of these species. Although numerous researchers have studied diseases on plant leaves, it is undeniable that timely diagnosis of diseases on olive leaves remains a difficult task. It is estimated that people have been cultivating olive trees for 6000 years, making it one of the most useful and profitable fruit trees in history. Symptoms that appear on infected leaves can vary from one plant to another or even between individual leaves on the same plant. Because olive groves are susceptible to a variety of pathogens, including bacterial blight, olive knot, Aculus olearius, and olive peacock spot, it has been difficult to develop an effective olive disease detection algorithm. For this reason, we developed a unique deep ensemble learning strategy that combines the convolutional neural network model with vision transformer model. The goal of this method is to detect and classify diseases that can affect olive leaves. In addition, binary and multiclassification systems based on deep convolutional models were used to categorize olive leaf disease. The results are encouraging and show how effectively CNN and vision transformer models can be used together. Our model outperformed the other models with an accuracy of about 96% for multiclass classification and 97% for binary classification, as shown by the experimental results reported in this study.

Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques

Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.

Whole-Body Low-Dose Multidetector-Row CT in Multiple Myeloma: Guidance in Performing, Observing, and Interpreting the Imaging Findings.

Multiple myeloma is a hematological malignancy of plasma cells usually detected due to various bone abnormalities on imaging and rare extraosseous abnormalities. The traditional approach for disease detection was based on plain radiographs, showing typical lytic lesions. Still, this technique has many limitations in terms of diagnosis and assessment of response to treatment. The new approach to assess osteolytic lesions in patients newly diagnosed with multiple myeloma is based on total-body low-dose CT. The purpose of this paper is to suggest a guide for radiologists in performing and evaluating a total-body low-dose CT in patients with multiple myeloma, both newly-diagnosed and in follow-up (pre and post treatment).

Survey on Identify the Agricultural Diseases Using Image Processing and Soft Computing Techniques

The agricultural land mass is more than just being a feeding sourcing in today’s world. Agriculture productivity defines the economy of India in a great manner. So, in plants, disease detection plays a vital role in agriculture field. Automatic disease detection approaches are used for detecting plant diseases during the initial stages. To identify the agricultural diseases using digital image based on various features like color, textures and shape. Research firm currently doing a research to detect and diagnosis agricultural diseases based on digital image. This survey provides a better understanding of the soft computing techniques and image processing used for researcher and farmers to identify the agricultural diseases. This survey highlights several diseases of agricultural plants like rice, apple, cucumber, graphs, banana, cherry, wheat and sugarcane. And also this analysis work provides the comparison analysis of different research techniques in terms of their merits and demerits along with numerical analysis.

Abstract 2098: Twist Fast Hybridization targeted methylation sequencing: a tunable target enrichment solution for methylation detection

Abstract DNA methylation is a genetic occurrence that plays a principal role in the epigenetic regulation of gene expression. Genome wide association studies (GWAS) have linked differential methylation patterns, especially at cytosines in CpG context, to disease states like cancer and obesity. Well-known chemical and enzymatic techniques are available to detect the methylation state of cytosines throughout the genome. With Next-Generation Sequencing (NGS) becoming increasingly affordable and high-throughput, methylation sequencing has become a popular and accessible approach for disease detection. Correspondingly, there is an increasing demand for targeted methylation sequencing techniques that enrich for CpG-rich genomic regions of interest. Such techniques have historically been inefficient due to the reduced sequence complexity caused by bisulfite or enzymatic library preparation methods. These methods involve the conversion of unmethylated cytosines to uracils, subsequently resulting in thymines after PCR amplification, and inherently increase similarities between library sequences in a given population. This effect causes issues with specificity during hybrid capture since baits and targets with repetitive sub-sequences can incidentally hybridize to non-targeted molecules during capture. In this poster, we present the Twist Targeted Methylation Sequencing Product, an end-to-end solution for preparing methyl-seq libraries and performing in-solution hybrid capture. We demonstrate benefits of using the NEBNext Enzymatic Methyl-seq Methylation Library Preparation protocol in our system, compared to traditional bisulfite treatment, and discuss approaches that were taken to maximize target enrichment specificity and uniformity. First, we introduce Twist's custom panel design approach, which leverages Twist's oligo manufacturing capabilities and uses predictive methods to remove probes most likely to cause poor capture performance. Second, we present methylation-specific Twist Fast Hybridization Target Enrichment protocol tunability, showing how FastHyb Wash Buffer 1 temperature and hybridization time can be used to manipulate performance metrics. Third, we introduce Twist's Methylation Enhancer, a novel blocker set that has shown to alleviate capture specificity issues across a wide range of methylation panels. Together, the data in this study showcase the Twist Targeted Methylation Sequencing Product as an efficient and flexible solution for custom methylation detection. Citation Format: Brenton I. Graham, Leonardo Arbiza, Kristin Butcher, Siyuan Chen, Richard Gantt, Sabina Gude, Rebecca Nugent, Christina Thompson, Ramsey Zeitoun. Twist Fast Hybridization targeted methylation sequencing: a tunable target enrichment solution for methylation detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2098.

Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: An Emerging Approach for Disease Detection and Treatment Optimization

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Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: An Emerging Approach for Disease Detection and Treatment Optimization.

In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Soon, tailored patient care via telemonitoring can improve clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.