Binary Image Classification Through an Optimal Topology for Convolutional Neural Networks

Deep learning applications in computer vision have expanded over the past years. Image classification, which is the fundamental of most algorithms in the field, has been of interest to many researchers. Advances in hierarchical feature extractions using convolutional neural networks as one of the deep learning architectures have enabled experts to improve the performance of classification significantly. In this work, an optimal binary classifier to distinguish cat and dog images was explored where various architectures and parameters were employed to achieve the best results. To design our experiment, we considered the architectures with two and three convolutional layers using two input image size when models were trained with and without Dropout against an identical dataset. The analysis demonstrated that an accuracy rate of 99.26% for the testing dataset was achieved from a three-layer model with an input image size of 32x32 with Dropout. The classification report of any models was produced to explore other metrics such as precision, recall, and F1-score, and they were aligned with the accuracy rates as this experiment was a balanced data situation

A Comprehensive Review of Deep Learning Architectures for Computer Vision Applications

The emergence of machine learning in the artificial intelligence field led the world of technology to make great strides. Today’s advanced systems with the ability of being designed just like human brain functions has given practitioners the ability to train systems so that they could process, analyze, classify, and predict different data classes. Therefore, the machine learning field has become a hot topic for scientists and researchers to introduce the best network with the highest performance for such mentioned purposes. In this article, computer vision science, image classification implementation, and deep neural networks are presented. This article discusses how models have been designed based on the concept of the human brain. The development of a Convolutional Neural Network (CNN) and its various architectures, which have shown great efficiency and evaluation in object detection, face recognition, image classification, and localization, are also introduced. Furthermore, the utilization and application of CNNs, including voice recognition, image processing, video processing, and text recognition, are examined closely. A literature review is conducted to illustrate the significance and the details of Convolutional Neural Networks in various applications

Recent Applications of Deep Learning Algorithms in Medical Image Analysis

Advances in deep learning have enabled researchers in the field of medical imaging to employ such techniques for various applications, including early diagnosis of different diseases. Deep learning techniques such as convolutional neural networks offer the capability of extracting invariant features from images that can improve the performance of most predictive models in medical and diagnostic imaging. This work concentrates on reviewing deep learning architectures along with medical imaging modalities where the crucial applications of such algorithms, including image classification and segmentation, are discussed. Also, brain imaging as a branch of medical imaging which allows scientists to explore the structure and function of the brain is explored, and the applications of deep learning to early diagnose Alzheimer’s Disease, and Autism as the most critical brain disorders are studied. Moreover, the recent research findings revealed that employing deep learning-based semantic segmentation techniques could significantly improve the accuracy of models developed for brain tumor detection. Such advances in early diagnosis of disorders and tumors encourage medical imaging practitioners to implement software applications assisting them to improve their decision-making process

Prevalence and correlates of psychiatric disorders in a national survey of Iranian children and adolescents

Objective: Considering the impact of rapid sociocultural, political, and economical changes on societies and families, population-based surveys of mental disorders in different communities are needed to describe the magnitude of mental health problems and their disabling effects at the individual, familial, and societal levels. Method: A population-based cross sectional survey (IRCAP project) of 30 532 children and adolescents between 6 and 18 years was conducted in all provinces of Iran using a multistage cluster sampling method. Data were collected by 250 clinical psychologists trained to use the validated Persian version of the semi-structured diagnostic interview Kiddie-Schedule for Affective Disorders and Schizophrenia-PL (K-SADS-PL). Results: In this national epidemiological survey, 6209 out of 30 532 (22.31%) were diagnosed with at least one psychiatric disorder. The anxiety disorders (14.13%) and behavioral disorders (8.3%) had the highest prevalence, while eating disorders (0.13%) and psychotic symptoms (0.26%) had the lowest. The prevalence of psychiatric disorders was significantly lower in girls (OR = 0.85; 95% CI: 0.80-0.90), in those living in the rural area (OR = 0.80; 95% CI: 0.73-0.87), in those aged 15-18 years (OR = 0.92; 95% CI: 0.86-0.99), as well as that was significantly higher in those who had a parent suffering from mental disorders (OR = 1.96; 95% CI: 1.63-2.36 for mother and OR = 1.33; 95% CI: 1.07-1.66 for father) or physical illness (OR = 1.26; 95% CI: 1.17-1.35 for mother and OR = 1.19; 95% CI: 1.10-1.28 for father). Conclusion: About one fifth of Iranian children and adolescents suffer from at least one psychiatric disorder. Therefore, we should give a greater priority to promoting mental health and public health, provide more accessible services and trainings, and reduce barriers to accessing existing services. © 2019 Tehran University of Medical Scienc

Prevalence and Correlates of Psychiatric Disorders in a National Survey of Iranian Children and Adolescents

Objective: Considering the impact of rapid sociocultural, political, and economical changes on societies and families, population-based surveys of mental disorders in different communities are needed to describe the magnitude of mental health problems and their disabling effects at the individual, familial, and societal levels. Method: A population-based cross sectional survey (IRCAP project) of 30 532 children and adolescents between 6 and 18 years was conducted in all provinces of Iran using a multistage cluster sampling method. Data were collected by 250 clinical psychologists trained to use the validated Persian version of the semi-structured diagnostic interview Kiddie-Schedule for Affective Disorders and Schizophrenia-PL (K-SADS-PL). Results: In this national epidemiological survey, 6209 out of 30 532 (22.31%) were diagnosed with at least one psychiatric disorder. The anxiety disorders (14.13%) and behavioral disorders (8.3%) had the highest prevalence, while eating disorders (0.13%) and psychotic symptoms (0.26%) had the lowest. The prevalence of psychiatric disorders was significantly lower in girls (OR = 0.85; 95% CI: 0.80-0.90), in those living in the rural area (OR = 0.80; 95% CI: 0.73-0.87), in those aged 15-18 years (OR = 0.92; 95% CI: 0.86-0.99), as well as that was significantly higher in those who had a parent suffering from mental disorders (OR = 1.96; 95% CI: 1.63-2.36 for mother and OR = 1.33; 95% CI: 1.07-1.66 for father) or physical illness (OR = 1.26; 95% CI: 1.17-1.35 for mother and OR = 1.19; 95% CI: 1.10-1.28 for father). Conclusion: About one fifth of Iranian children and adolescents suffer from at least one psychiatric disorder. Therefore, we should give a greater priority to promoting mental health and public health, provide more accessible services and trainings, and reduce barriers to accessing existing services

Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 2·1% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 13·0 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (18·8%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (50·2%) of 11 476 included individuals were female and 5720 (49·8%) were male. Sex data were missing for 372 (3·1%) of 11 848 individuals. Median age at registry entry was 9·6 years (IQR 5·8-13·2). 10 099 (89·9%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (10·1%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (5·2%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [92·4%] of 10 202) than in children and adolescents from non-high-income countries (199 [48·0%] of 415). 3414 (31·6%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (72·4%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 5·00 mmol/L (IQR 4·05-6·08). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation. Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life

Comparison of the effect of argon, hydrogen, and nitrogen gases on the reduced graphene oxide-hydroxyapatite nanocomposites characteristics

In this study, the effect of the argon, nitrogen, and hydrogen gases on the final properties of the reduced graphene oxide-hydroxyapatite nanocomposites synthesized by gas injected hydrothermal method was investigated. Four samples were synthesized, which in the first sample the pressure was controlled by volume change at a constant concentration. In subsequent samples, the pressure inside the autoclave was adjusted by the injecting gases. The initial pressure of the injected gases was 10 bar and the final pressure considered was 25 bar. The synthesized powders were consolidated at 950 °C and 2 MPa by spark plasma sintering method. The final samples were subjected to Vickers indentation analysis. The findings of this study indicate that the injection of argon, hydrogen, and nitrogen gases improved the mechanical properties of the nanocomposites. Injection of gases increased the crystallinity and particle size of hydroxyapatite, and this increase was greater for nitrogen gas than for others. Injection of these gases increased the rate of graphene oxide reduction and in this case the effect of nitrogen gas was greater than the others. [Figure not available: See fulltext.]There is no official funding. XRD and FTIR were performed at Tarbiat Modares University (Iran). Raman spectroscopy and Indentation testing were performed at ICV-CSIC (Spain), and the SEM and TEM analyses were performed at Aarhus University (Denmark)

Prevalence, comorbidity and predictors of anxiety disorders among children and adolescents

Childhood anxiety may lead to serious health consequences in later life. The present study provides the prevalence, comorbidity, and predictors of anxiety disorders among children and adolescents. This was a cross-sectional national project that was implemented on 28,698 children and adolescents in Iran. Participants entered the study by multistage cluster sampling with an equal number of each gender and three age groups (6-9, 10-14, and 15-18 years) within each cluster. The tools used in this research were the demographic questionnaire and K-SADS-PL. To analyze the data logistic regression and chi-square tests were used in SPSS (ver. 16). The prevalence of anxiety disorder in children and adolescents was 13.2 in boys and 15.1 in girls. Furthermore, gender, age, place of residence and history of psychiatric hospitalization of parents could predict anxiety disorders. Anxiety disorders had comorbidity with behavioral disorders, neurodevelopmental disorders, mood disorders, psychotic disorders, substance abuse disorders, and elimination disorders. According to our findings in this study, anxiety disorders affect the performance, health and life of children and adolescents, identifying the childhood anxiety, as well as finding diseases that are associated with anxiety disorders, can help in the prevention of the disorder. © 2020 Elsevier B.V

Prevalence of elimination disorders and comorbid psychiatric disorders in Iranian children and adolescents

PURPOSE: Currently, there is a paucity of studies on the prevalence of Elimination Disorders among Iranian children and adolescents. Due to the ongoing need to monitor the health status of these children and adolescents, the present study aims to investigate the prevalence of Elimination Disorders and comorbid disorders in Iranian children and adolescents. METHODS: In this cross-sectional study, 29,781 children and adolescents age 6 to 18 years old were selected and studied from all the provinces in Iran. The sampling was carried out by employing a multistage cluster sampling method, and several clinical psychologists using semi-structured interviews collected the data. Furthermore, clinical psychologists collected demographic information (including information about gender, age, place of residence, education level, and parental education level). The collected data were analyzed using SPSS version 20. RESULTS: Generally, the prevalence of Elimination Disorders was found to be 5.4 covering both enuresis (p= 5.4, 95 CI = 5.1-5.7) and encopresis (p= 0.13, 95 CI = 0.09-0.2). The total prevalence of comorbid disorders was 38, and among the comorbid disorders, Attention Deficit Hyperactivity Disorder (ADHD) (p= 11, 95 CI = 9.5-12.7) and Separation Anxiety (p= 10.6, 95 CI = 9.1-12.2) were the most prevalent. CONCLUSION: The prevalence of Elimination Disorders in Iranian children and adolescents is moderate compared to similar studies elsewhere. As for comorbid disorders, ADHD and Separation Anxiety were found to be the most prevalent disorders. Since Elimination Disorders coexist with psychiatric disorders in children, further studies of these comorbidities may give better insight into the treatment and prognosis of Elimination Disorders. © 2021 - IOS Press. All rights reserved

Prevalence of elimination disorders and comorbid psychiatric disorders in Iranian children and adolescents

PURPOSE: Currently, there is a paucity of studies on the prevalence of Elimination Disorders among Iranian children and adolescents. Due to the ongoing need to monitor the health status of these children and adolescents, the present study aims to investigate the prevalence of Elimination Disorders and comorbid disorders in Iranian children and adolescents. METHODS: In this cross-sectional study, 29,781 children and adolescents age 6 to 18 years old were selected and studied from all the provinces in Iran. The sampling was carried out by employing a multistage cluster sampling method, and several clinical psychologists using semi-structured interviews collected the data. Furthermore, clinical psychologists collected demographic information (including information about gender, age, place of residence, education level, and parental education level). The collected data were analyzed using SPSS version 20. RESULTS: Generally, the prevalence of Elimination Disorders was found to be 5.4% covering both enuresis (p = 5.4, 95% CI = 5.1-5.7) and encopresis (p = 0.13, 95% CI = 0.09-0.2). The total prevalence of comorbid disorders was 38%, and among the comorbid disorders, Attention Deficit Hyperactivity Disorder (ADHD) (p = 11, 95% CI = 9.5-12.7) and Separation Anxiety (p = 10.6, 95% CI = 9.1-12.2) were the most prevalent. CONCLUSION: The prevalence of Elimination Disorders in Iranian children and adolescents is moderate compared to similar studies elsewhere. As for comorbid disorders, ADHD and Separation Anxiety were found to be the most prevalent disorders. Since Elimination Disorders coexist with psychiatric disorders in children, further studies of these comorbidities may give better insight into the treatment and prognosis of Elimination Disorders