Biopsychosocial perspective of ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is considered a major public health problem, not only for its high prevalence but also because the symptoms have an impact on activities in daily life at both familial and school levels as well as on a general social level. Clinical evaluation of ADHD was based on the diagnostic criteria of the International Classification of Diseases ICD 10, Diagnostic and Statistical Manual of Mental Disorders DSM IV and comorbidity phenomena. Therefore, it has not yet developed into evaluations any more comprehensive than activity limitations and participation restrictions from a biopsychosocial model of disability, as proposed by the International Classification of Functioning, Disability and Health ICF. Thus, it is necessary to start assessment processes of children with ADHD using the functionality and performance components proposed by the ICF, allowing a new approach and a greater understanding of the health status of this population from a more holistic perspective in relation to the disability. Objective: To identify the theoretical elements that justify the importance of addressing ADHD from a biopsychosocial perspective as proposed by the evaluation of the ICF model, ensuring comprehensive assessment processes. This article is the result of a theoretical review addressed in research projects around the design, validity and reliability of assessment instruments of activity limitations and participation restrictions in children with ADHD.

An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary.The situation is very complex as the COVID-19 test kits are limited,therefore,more diagnostic methods must be developed urgently.A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography(CT),where any chest anomalies(e.g.,lung inflammation)can be easily identified.Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19.Motivated by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT images.However,the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs,which is not widely available in several countries.This paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks(CNNs),which does not require a custom hardware to run compared to currently available CNN models.The proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU(0.54%of AlexNet parameters).This model is highly beneficial for countries where high-end GPUs are luxuries.Experimental outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.

Optimized Convolutional Neural Network Models for Skin Lesion Classification

Skin cancer is one of themost severe diseases,andmedical imaging is among themain tools for cancer diagnosis.The images provide information on the evolutionary stage,size,and location of tumor lesions.This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks(CNNs)in distinguishing different skin lesions.The CNNs are based on transfer learning,taking advantage of ImageNet weights.Accordingly,in each experiment,different workflow stages are tested,including data augmentation and fine-tuning optimization.Three CNN models based on DenseNet-201,Inception-ResNet-V2,and Inception-V3 are proposed and compared using the HAM10000 dataset.The results obtained by the three models demonstrate accuracies of 98%,97%,and 96%,respectively.Finally,the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%.The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.