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In 1952, the American Psychiatric Association's Committee on Nomenclature and Statistics published the first edition of diagnostic and statistical manual of mental disorders (DSM-I). Five editions have been published since then: DSM-II (1968), DSM-III (1980), a revised DSM-III, DSM-III-R (1987), DSM-IV (1994), and DSM-IV-TR (TR stands for Text Revision) (2000). The purpose of this article was to present a brief review about changes in DSM (from DSM-IV-TR to DSM-5). Publication of the fifth edition of diagnostic and statistical manual of mental disorders (DSM-5) in May 2013 will be marked as one of the most anticipated events in the mental health field. As part of the development process, based on concerns about the reliability of the proposed attenuated psychosis syndrome and mixed anxiety depressive disorder in the field trials, these two conditions are being recommended for further study in section III. A footnote was also added to the major depressive disorder criteria to clarify the difference between normal bereavement associated with a significant loss and a diagnosis of a mental disorder. Among the other significant changes in this study was personality disorders diagnostic criteria for pedophilic disorder which was modified. Communication disorders now include two diagnoses: Language disorders and speech disorders. Simple somatic symptom disorder was a milder form of complex somatic symptom disorder; the two have now been combined as a single disorder namely somatic symptom disorder. Non-suicidal self-injury disorder and persistent complex bereavement disorder. In the fifth edition of diagnostic and statistical manual of mental disorders (DSM-5) that has been published in May 2013, there are several significant changes in diagnostic criteria, some classifications, subscales, and proposed new terms.

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The Diagnostic and Statistical Manual of Mental Disorders ( DSM ) has provided a standard language that physicians, scientists, and public health specialists use to communicate about mental disorders. This guideline was revised for the first time in 1986 and its last edition (fifth edition) was published in 2013. The most important modifications in the DSM-5 include the change in font, reexamination of the definition of psychological disorder, the order of categories, considerations for lifetime growth and developmental issues, categorization of disorders with greater emphasis on neuroscience and less emphasis on symptom emergence, differentiation between main diagnosis and the reason for referral, determination of temporary or absolute diagnosis, new categories of other specified and unspecified disorders, tendency toward dimensional evaluation, decrease in diagnosis-centered system, emphasis on cultural issues, emphasis on gender issues, and reexamination of diagnosis criteria. The aim of the present article was a critical, applied, and integrated investigation into these modifications. The new version received the most criticisms regarding extreme medicalization of normal issues. The main controversies regarding this version are related to the definition of some disorders, diagnostic inflation, and inappropriate impact of drug companies. In the final section of the article, the future perspectives of psychological disorder categories are explored. It seems that the Research Domain Criteria (RDoC) Project will play an important role in psychological disorder categorization in the future.

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Aim and Background: Alzheimer 's disease is the most common form of dementia which has caused disorder in memory. Cerebral palsy and posttraumatic stress disorder (veterans of war, warriors, armed forces) play an important role in increasing the risk of Alzheimer's disease. The nature of the large dimensions of neural data, as well as the small number of available samples, make for an accurate computer diagnostic system. The aim of this study was to apply deep neural networks to develop an automatic disease diagnosis system.
Methods and Materials: In this research, studies on magnetic resonance imaging of war veterans are done by python Software. In the proposed model, in the proposed model, 10% of the images of the data base were selected for training. In the first stage, the training is from deep learning with Convolutional network to extract the features, then in the second stage, in order to classify the health status based on the learned features.
Findings: The results of the analysis are also compared with the results presented in previous Studies. The proposed method has higher detection accuracy than the existing Ones, which increases the accuracy of detection in many cases.
Conclusions: The results of this study showed that using intelligent methods based on deep learning can accurately diagnose the disease.