Neural network-based approach for the non-invasive diagnosis and classification of hepatotropic viral disease

2012 ◽  
Vol 6 (18) ◽  
pp. 3265-3273 ◽  
Author(s):  
S. Ansari ◽  
J. Ahmad ◽  
I. Shafi ◽  
S. Ismail Shah
Keyword(s):  
Neural Network ◽  
Viral Disease ◽  
Non Invasive ◽  
Diagnosis And Classification

Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

2021 ◽  
Vol 137 ◽  
pp. 106861
Author(s):  
Deepa Joshi ◽  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Dilip K. Prasad ◽  
S.V. Amitha Mithra ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Optical Coherence ◽  
Label Free ◽  
Non Invasive ◽  
Rice Seeds

Pathophysiology, Classification, Clinical Diagnosis and Treatment of Primary Epiretinal Membrane

2018 ◽  
pp. 262-267
Keyword(s):  
Clinical Examination ◽  
Epiretinal Membrane ◽  
Posterior Vitreous Detachment ◽  
Internal Limiting Membrane ◽  
Non Invasive ◽  
Diagnosis And Classification ◽  
Pars Plana ◽  
Vitreomacular Interface

Epiretinal membrane (ERM), also known as macular pucker, premacular fibroplasia, premacular gliosis, or cellophane maculopathy is a common vitreoretinal interface pathology that can result in mild to moderate visual impairment with an impact on the quality of life. ERM can be classified as primary “idiopathic” or secondary. Most ERMs occur in individuals older than 50 years, and the prevalence of ERM increases as age increases. The pathological mechanisms are not entirely known, however, the posterior vitreous detachment is thought to be key. Diagnosis and classification of ERM are based on clinical examination findings. However, high resolution spectral domain-optic coherence tomographies (SD-OCTs) have proven to be more sensitive than clinical examination for the diagnosis of numerous disorders of the vitreomacular interface, including ERM. SD-OCTs enable the pre-and postoperative comparison of macular structures in a non-invasive examination. In treatment, surgical intervention entails pars plana vitrectomy with ERM removal with or without internal limiting membrane (ILM) removal. Good visual recovery was present in most patients after surgery.

scholarly journals Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Naiping Li ◽  
Yongfang Jiang ◽  
Jin Ma ◽  
Bo He ◽  
Wei Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Liver Diseases ◽  
High Performance ◽  
Liver Function Tests ◽  
General Regression Neural Network ◽  
Diagnosis And Classification ◽  
Tenfold Cross Validation ◽  
Alcoholic Liver Diseases ◽  
General Regression

Alcoholic liver diseases cause high incidence of death worldwide. However, computational diagnosis and classification of alcoholic hepatitis have not yet been established. In this study, we used general regression neural network (GRNN) model with a high-performance classification ability to diagnose and classify alcohol hepatitis. We used tenfold cross-validation to demonstrate the error rate of networks. The results show an accuracy of 80.91% of the back diagnosis in 110 patients and the accuracy of 81.82% of predicting-diagnosis in 11 patients referring to the clinical diagnosis made by a group of experts. This study suggested that using the liver function tests as the input layer variables of GRNN model could accurately diagnose and classify alcoholic liver diseases.

scholarly journals Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5480
Author(s):  
Agata Kirjanów-Błażej ◽  
Aleksandra Rzeszowska
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Conveyor Belt ◽  
Belt Conveyor ◽  
Wear Process ◽  
Non Invasive ◽  
Changes Over Time ◽  
Transported Material ◽  
Matlab Programming

Non-invasive conveyor belt diagnostics in damage detection allows significant reductions of the costs related to belt replacement, as well as the evaluation of belt usability and wear degree changes over time. As a result, it increases safety in the location where the belt is used. Depending on the location of a belt conveyor, its length or the type of the transported material, the belt may undergo wear at different rates, albeit the wear process itself is inevitable. This article presents an artificial intelligence-based approach to the classification of conveyor belt damage. A two-layer neural network was implemented in the MATLAB programming language, with the use of a Deep Learning Toolbox set. As a result of the optimization of the created network, the effectiveness of operation was at the level of 80%.

scholarly journals A Novel Convolution Neural Network for the Diagnosis and Classification of Rosacea (Preprint)

10.2196/23415 ◽  
2020 ◽  
Author(s):  
Zhixiang Zhao ◽  
CheMing Wu ◽  
Shuping Zhang ◽  
Fanping He ◽  
Fangfen Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Diagnosis And Classification

scholarly journals Diagnosis and Classification of Type II Diabetes based on Multilayer Neural Network

2021 ◽  
pp. 3744-3758
Author(s):  
Ekhlas S. Nasser ◽  
Faten Abd Ali Dawood
Keyword(s):  
Neural Network ◽  
Type Ii Diabetes ◽  
World Health ◽  
Diabetic Patients ◽  
Second Phase ◽  
Haemoglobin A1c ◽  
Type Ii ◽  
Proposed Model ◽  
Diagnosis And Classification

     Diabetes is considered by the World Health Organization (WHO) as a main health problem globally. In recent years, the incidence of Type II diabetes mellitus was increased significantly due to metabolic disorders caused by malfunction in insulin secretion. It might result in various diseases, such as kidney failure, stroke, heart attacks, nerve damage, and damage in eye retina. Therefore, early diagnosis and classification of Type II diabetes is significant to help physician assessments. The proposed model is based on Multilayer Neural Network using a dataset of Iraqi diabetes patients obtained from the Specialized Center for Endocrine Glands and Diabetes Diseases. The investigation includes 282 samples, of which 240 are diabetic and 42 are non-diabetic patients. The model consists of three main phases.  In the first phase, two steps are applied as a pre-processing for the dataset, which include statistical analysis and missing values handling. In the second phase, feature extraction is used for diabetes Type II using three main features, reflecting measurements of three blood parameters (C. peptide, fasting Blood Sugar, and Haemoglobin A1C). Finally, classification and performance evaluation are implemented using Feed Forward Neural Network algorithm. The experimental results of the performance of the proposed model showed 98.6% accuracy for diabetes classification.

scholarly journals Liquid Biomarkers for Improved Diagnosis and Classification of CNS Tumors

2021 ◽  
Vol 22 (9) ◽  
pp. 4548
Author(s):  
Severa Bunda ◽  
Jeffrey A. Zuccato ◽  
Mathew R. Voisin ◽  
Justin Z. Wang ◽  
Farshad Nassiri ◽  
...  
Keyword(s):  
Liquid Biopsy ◽  
Cns Tumors ◽  
Invasive Technique ◽  
Liquid Biopsies ◽  
Major Step ◽  
Tumor Biopsy ◽  
Non Invasive ◽  
Diagnosis And Classification ◽  
Cns Malignancies

Liquid biopsy, as a non-invasive technique for cancer diagnosis, has emerged as a major step forward in conquering tumors. Current practice in diagnosis of central nervous system (CNS) tumors involves invasive acquisition of tumor biopsy upon detection of tumor on neuroimaging. Liquid biopsy enables non-invasive, rapid, precise and, in particular, real-time cancer detection, prognosis and treatment monitoring, especially for CNS tumors. This approach can also uncover the heterogeneity of these tumors and will likely replace tissue biopsy in the future. Key components of liquid biopsy mainly include circulating tumor cells (CTC), circulating tumor nucleic acids (ctDNA, miRNA) and exosomes and samples can be obtained from the cerebrospinal fluid, plasma and serum of patients with CNS malignancies. This review covers current progress in application of liquid biopsies for diagnosis and monitoring of CNS malignancies.

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