scholarly journals A Novel System for the Identification of Diabetic Retinopathy using Computer Based Statistical Classification

2021 ◽  
Vol 12 (3) ◽  
pp. 4044-4059
Author(s):  
Sunil S S Et.al
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Early Stage ◽  
Diabetic Patients ◽  
Statistical Classification ◽  
Computer Based ◽  
Detection And Diagnosis ◽  
Different Characteristics ◽  
Early Detection And Diagnosis

Diabetes Retinopathy (DR) is an eye disorder that affects the human retina due to increased insulin levels in the blood. Early detection and diagnosis of DR is essential in the optimal treatment of diabetic patients. The current research is to develop controls for identifying different characteristics and differences in colour  retina and using different classifications. This therapeutic approach describes diabetes recovery from data collected from multiple fields including DRIDB0, DRIDB1, MESSIDOR, STARE and HRF. Here  machine learning, neural networks and deep learning algorithms issues are addressed with related topics such as Sensitivity, Precision, Accuracy, Error,   Specificity and F1-score, Mathews Correlation Coefficient (MCC) and coefficient of kappa are compared. Finally due to the deep learning strategy the results were more effective compared to other methods. The system can help ophthalmologists, to identify the symptoms of diabetes at an early stage, for better treatment and to improve the quality of life biology.

scholarly journals Early-Stage Detection of Cancer in Breast Using Artificial Intelligence

2021 ◽  
Vol 11 (2) ◽  
pp. 2016-2028
Author(s):  
M.N. Vimal Kumar ◽  
S. Aakash Ram ◽  
C. Shobana Nageswari ◽  
C. Raveena ◽  
S. Rajan
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Early Stage ◽  
Deep Learning Algorithm ◽  
Common Cancer ◽  
Abnormal Cells ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

One of the deadly diseases among humans is Cancer, which occurs almost anywhere in the human body. Cancer is caused by the cells that spread into the surrounding tissues by dividing itself uncontrollably. Breast Cancer is the most common cancer among women. Early detection and diagnosis of breast cancer are treatable and curable. Many women have no symptoms for this cancer at an early stage. The abnormal cells in the breast will risk for the development of breast cancer. So, it is important for women to regularly examine their breast. Technologies can be utilized in a smarter way with Artificial Intelligence techniques to assist the women during their examination of the breast at their living place to avoid the risk of breast cancer. The main aim is to develop a lowcost self-examining device for the detection of breast cancer and abnormality in the breast using an efficient optical method, Deep-learning algorithm and Internet of Things.

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

scholarly journals The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

2020 ◽  
Author(s):  
Jingyan Qiu ◽  
Linjian Li ◽  
Yida Liu ◽  
Yingjun Ou ◽  
Yubei Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Early Stage ◽  
Training Transfer ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Model ◽  
The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

Utility of Neonatal Findings in Early Diagnosis of a Case of Haberland Syndrome

2021 ◽  
Author(s):  
Morgan R. Sturgis ◽  
Kathryn E. Wrobel ◽  
Gianna N. Bosco ◽  
Carolyn H. Jones
Keyword(s):  
Quality Of Life ◽  
Symptom Severity ◽  
Clinical Presentation ◽  
Male Infant ◽  
Encephalocraniocutaneous Lipomatosis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis ◽  
Scalp Skin ◽  
Congenital Syndrome

AbstractHaberland syndrome or encephalocraniocutaneous lipomatosis (ECCL) is a rare, congenital syndrome characterized by lipomas and noncancerous tumors of the scalp, skin, and eyes, in addition to intellectual disability, early onset seizures, and ectomesodermal dysgenesis. The diagnosis of ECCL is classically made by clinical presentation, imaging, and histopathological findings, but due to the spectrum of clinical presentation and symptom severity, diagnosis is often delayed until adolescence or adulthood. Here we present a newborn male infant, one of the earliest case diagnoses to our knowledge, with a unique constellation of physical exam and neuroimaging findings consistent with this diagnosis. We aim to address important neonatal findings to aid in early detection and diagnosis of this unique disease, which is thought to improve clinical outcomes and patient quality of life.

Early detection and diagnosis using deep learning

2021 ◽  
pp. 191-217
Author(s):  
Deepak Kumar Sharma ◽  
Saakshi Bhargava ◽  
Aashna Jha ◽  
Pawan Singh
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

KNN Loss and Deep KNN

2021 ◽  
Vol 182 (2) ◽  
pp. 95-110
Author(s):  
Linh Le ◽  
Ying Xie ◽  
Vijay V. Raghavan
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Nearest Neighbor ◽  
Learning Strategy ◽  
Feature Space ◽  
Kernel Functions ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Map Data

The k Nearest Neighbor (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we present two loss functions, namely KNN Loss and Fuzzy KNN Loss, to quantify the quality of neighborhoods formed by KNN with respect to supervised learning, such that minimizing the loss function on the training data leads to maximizing KNN decision accuracy on the training data. We further present a deep learning strategy that is able to learn, by minimizing KNN loss, pairwise similarities of data that implicitly maps data to a feature space where the quality of KNN neighborhoods is optimized. Experimental results show that this deep learning strategy (denoted as Deep KNN) outperforms state-of-the-art supervised learning methods on multiple benchmark data sets.

STATISTICAL-BASED SUB-BAND FILTERING TECHNIQUE FOR DIGITAL MAMMOGRAM ENHANCEMENT

2003 ◽  
Vol 15 (04) ◽  
pp. 150-156 ◽  
Author(s):  
HONG-DUN LIN ◽  
KANG-PING LIN ◽  
BEING-TAU CHUNG
Keyword(s):  
Breast Cancer ◽  
Breast Image ◽  
Comparison Results ◽  
Filtering Technique ◽  
Mammogram Image ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis ◽  
Detect Breast Cancer ◽  
Breast Gland

Early detection and diagnosis of breast cancer markedly increases survival rate. Digital mammography is believed to help breast image experts to detect breast cancer early. Accurate diagnosis also depends on the quality of the image presented to the experts. This study addresses the quality improvement of the image. A statistically based sub-band filtering method is applied to enhance the mass and calcification shown in the digital mammograms by inhibiting noise. The method is based on sub-band transformation due to its decomposition characteristic and includes two steps: noise inhibition and boundary enhancement. Contrast ratios and frequency responses are measured to evaluate the enhancement performance and the distortion affect, respectively, to validate the effectiveness of the proposed technique. By comparing the enhancement performance of proposed and conventional methods, a phantom mammogram image that consists of similar mammographic microcalcifications, breast gland and well-circumscribed mass is designed for simulation experiment. Moreover, the real mammograms with microcalcifications are also applied on representing the efficient enhancement ability by the proposed method in this paper. Results in this study demonstrate that the proposed method improves the quality of the image more than other enhancement methods, according to these two criteria. The comparison results show that not only the image quality is improved but also within less image distortion.

scholarly journals Detection of COVID-19 from CT scan images: A spiking neural network-based approach

2021 ◽  
Author(s):  
Avishek Garain ◽  
Arpan Basu ◽  
Fabio Giampaolo ◽  
Juan D. Velasquez ◽  
Ram Sarkar
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Graphics Processing Units ◽  
Early Stage ◽  
Medical Image Analysis ◽  
Global Pandemic ◽  
Radiological Images ◽  
Chest X Ray ◽  
Computer Based ◽  
Graphics Processing

AbstractThe outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

scholarly journals Diabetic neuropathies

2000 ◽  
Vol XXXII (3-4) ◽  
pp. 59-67
Author(s):  
E. I. Bogdanov ◽  
V. V. Talantov ◽  
R. Z. Mukhamedzyanov
Keyword(s):  
Detection Rate ◽  
Early Stage ◽  
Diabetic Neuropathies ◽  
Myocardial Infarctions ◽  
Diabetic Patients ◽  
Treatment And Prevention ◽  
Patients With Diabetes ◽  
Pathogenetic Therapy ◽  
Clinical Variants

Diabetic neuropathies (DN) are among the most frequent and serious complications of diabetes [57, 9]. The detection rate of DN in diabetic patients varies greatly depending on their type, selected clinical and instrumental diagnostic criteria and, according to various researchers, ranges from 10 to 90% [16, 33]. At the same time, 1/3 of the polyneuropathies recognized in the neurological clinic are diabetic. In about 10% of cases, neuropathic symptoms are key in the diagnosis of diabetes [25]. DN has not only severe subjective manifestations and pronounced impairments that are objectively detected in them, but also leads to the development of diabetic foot syndrome - the cause of 50 - 70% of cases of all non-traumatic amputations of the legs [3]. In addition, in patients with diabetic visceral autonomic neuropathy, the syndrome of "sudden death" is much more common, and the frequency of painless myocardial infarctions and ischemic strokes is high. Clinical variants of DN are often the main causes of reduced quality of life, disability and disability in patients with diabetes mellitus. It is extremely important to diagnose the early stage of DN, when it is easier to achieve a therapeutic effect through the earliest and most stable provision of optimal control of an adequate level of glycemia and the appointment of means of pathogenetic therapy. Isolation of forms of DN, their diagnosis, treatment and prevention is an urgent clinical task.

scholarly journals Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mustafa Ghaderzadeh ◽  
Farkhondeh Asadi
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Diagnostic Methods ◽  
Efficient System ◽  
Current State ◽  
Radiological Images ◽  
Research Population ◽  
Radiologic Image ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Introduction. The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population. Result. This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. Conclusion. The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.

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