An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm

The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.

Trichotillomania in an Autistic Spectum Disroder: Single Case Intervention

The following work addresses trichotillomania and dermatillomania, both symptoms of the impulses control, in a 11 years old girl, who courses the sixth grade in a private elementary school and with a diagnose of first degree autism spectrum disorder and Attention Deficit Hyperactivity Disorder (ADHD) as a comorbidity. The objective was to reduce the frequency of tearing her hair and the skin imperfections in the school context throughout an intervention based on cognitive behavioral techniques. Within the used methodology to analyze the case an exhaustive evaluation of the patient has been performed using grade observation records, before and during the intervention period in order to systematize the whole process. The intervention techniques used where Token Economy and self-instruction. The results show a progressive improvement of the symptoms, reflected in the diminish of frequency of the behaviors registered that were conducted. In spite of the limited time for the intervention for these kinds of behaviors and the base line phase, it was possible to get to know the girl well and establish bonds with her, in spite of her condition, which can be noted in a better adaptation on her school context. This work seeks to favor the increase of research on this disorder since there is information related on the etiological factors, but it still is not enough, likewise, the related information on these disorders and its possible comorbidities is useful to continue with the advance on the treatments in this area.