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Introduction: Cognitive disorders, characterized by transient stages and potential Alzheimer's disease, are influenced by changes in iron deposits in the brain. These changes can lead to toxicity and neuron death. Quantitative susceptibility mapping is used to accurately represent these changes, allowing for a more accurate evaluation of the time window of each cognitive disorder stage and the need for targeted treatment.Material and Methods: The Alzheimer's Disease Neuroimaging Initiative research database was used to download the data and eight healthy participants and twenty-one participants with cognitive disorders based on MMSE cognitive test scores in 5 groups of cognitively normal, Subjective Memory Concern , Early Mild Cognitive Impairment, Late Mild Cognitive Impairment and Alzheimer's disease were included in this study. Quantitative Susceptibility Mapping processing was performed using the SEPIA toolbox in MATLAB, and segmentation was performed using FSL software. Finally, statistical analyzes were performed using SPSS V26 software.Results: Statistically significant changes were observed in the QSM values of the right thalamus (p-value = 0.043) in the LMCI group and the right hippocampal nucleus (p-value = 0.050) in the control group.Conclusion: After one year, the right hippocampal nucleus shows increased iron accumulation in healthy individuals, suggesting that the nucleus is susceptible to the highest rate of iron deposition in healthy individuals. Based on this result, the hypothesis that iron deposits are the cause of the unknown cause-and-effect relationship between iron deposits and Alzheimer's disease may be confirmed.

Abstract Introduction The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN). Material and method MR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system’s performance is evaluated by categorizing glioblastoma patients based on hypoxia. Results The results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy. Conclusion The results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.

ABSTRACT Introduction Strong indications support the notion that idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) acts as a precursor to multiple α‐synucleinopathies, including Parkinson's disease and dementia with Lewy bodies. Despite numerous investigations into the alterations in cortical thickness and the volume of subcortical areas associated with this condition, comprehensive studies on the cortical surface morphology, focusing on gyrification and sulcal depth changes, are scarce. The purpose of this research was to explore the cortical surface morphology in individuals with probable iRBD (piRBD), to pinpoint early‐phase diagnostic markers. Methods This study included 30 piRBD patients confirmed using the RBD Screening Questionnaire (RBDSQ) and 33 control individuals selected from the Parkinson's Progression Markers Initiative (PPMI) database. They underwent neurophysiological tests and MRI scans. The FreeSurfer software was utilized to estimate cortical thickness (CTH), cortical and subcortical volumetry, local gyrification index (LGI), and sulcus depth (SD). Subsequently, these parameters were compared between the two groups. Additionally, linear correlation analysis was employed to estimate the relationship between brain morphological parameters and clinical parameters. Results Compared to the healthy control (HC), piRBD patients exhibited a significant reduction in CTH, LGI, and cortical volume in the bilateral superior parietal, lateral occipital, orbitofrontal, temporo‐occipital, bilateral rostral middle frontal, inferior parietal, and precentral brain regions. Moreover, a significant and notable correlation was observed between CTH and Geriatric Depression Scale (GDS), letter–number sequencing (LTNS), the Benton Judgment of Line Orientation (BJLO) test, and the symbol digit modalities test (SDMT) in several brain regions encompassing the motor cortex. Conclusion Patients with piRBD displayed widespread atrophy in various brain regions, predominantly covering the motor and sensory cortex. Furthermore, LGI could serve as a prognostic biomarker of disease's progression in piRBD.

Introduction: Iron deposition is vital for damaging neurons and causing different cognitive disorders. Today, using the quantitative susceptibility mapping (QSM) technique, iron deposits in other brain areas can be assessed and measured. This study aimed to identify changes in iron deposition of 12 brain nuclei through different stages of dementia using the QSM technique to introduce biomarkers for the early detection of cognitive disorders. Material and Methods: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was used to download data. A 3T MRI scanner scanned thirty-five participants with normal cognition and forty-six patients with cognitive disorders who were classified into four groups based on the severity of the condition. QSM processing determined twelve regions of interest (ROIs) by automatic nuclei segmentation and statistical analysis performed in these groups’ MRI images. Results: Based on previous findings, QSM values ​​increase proportionally to iron deposition.In this study, the increase in the QSM values ​​of different nuclei of the early mild cognitive impairment (EMCI) stage indicates iron deposition in these participants. In the EMCI group, The QSM value of the bilateral thalamus (P

Abstract Rapid eye movement behavior disorder (RBD) is a parasomnia characterized by the loss of skeletal muscle atonia during the rapid eye movement (REM) sleep phase. On the other hand, idiopathic RDB (iRBD) is considered the prelude of the various α‐synucleinopathies, including Parkinson's disease (PD), dementia with Lewy bodies and multiple system atrophy. Consequently, over 40% of patients eventually develop PD. Recent neuroimaging studies utilizing structural magnetic resonance imaging (s‐MRI), diffusion‐weighted imaging (DWI), and functional magnetic resonance imaging (fMRI) with graph theoretical analysis have demonstrated that patients with iRBD and Parkinson's disease have extensive brain abnormalities. Thus, it is crucial to identify new biomarkers that aid in determining the underlying physiopathology of iRBD group. This review was conducted systematically on the included full‐text articles of s‐MRI, DWI, and fMRI studies using graph theoretical analysis on patients with iRBD, per the procedures recommended by Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA). The literature search was conducted through the PubMed and Google scholar databases concentrating on studies from September to January 2022. Based on the three perspectives of integration, segregation, and centrality, the reviewed articles demonstrated that iRBD is associated with segregation disorders in frontal and limbic brain regions. Moreover, this study highlighted the need for additional longitudinal and multicenter studies to better understand the potential of graph metrics as brain biomarkers for identifying the underlying physiopathology of iRBD group.

Introduction: Accurate segmentation of brain tissue in magnetic resonance imaging (MRI) is an important step in the analysis of brain images. There are automated methods used to segmentation the brain and minimize the disadvantages of manual segmentation, including time consuming and misinterpretations. These procedures usually involve a combination of skull removal, bias field correction, and segmentation. Therefore, segmented tissue quality assessment segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is required for the analysis of neuroimages. Material and Methods: This paper presents the performance evaluation of three automatic methods brain segmentation, fluid and white matter suppression [FSL, Freesurfer (FreeSurfer is an open source package for the analysis and visualization of structural, functional, and diffusion neuroimaging data from cross-sectional and longitudinal studies) and SPM12 (Statistical Parametric Mapping)]. Segmentation with SPM12 was performed on three tissue probability maps: i) threshold 0.5, ii) threshold 0.7 and iii) threshold 0.9. In order to compare and evaluate the automatic methods, the reference standard method, i.e., manual segmentation, was performed by three radiologists. Results: Comparison of GM, WM and CSF segmentation in MR images was performed using similarities between manual and automatic segmentation. The similarity between the segmented tissues was calculated using diagnostic criteria. Conclusion: Several studies have examined the classification of GM, WM, and CSF using software packages. In these studies, different results have been obtained depending on the type of method and images used and the type of segmented tissues. In this study, the evaluation of the segmentation of these packages with reference standard method is performed. The results can help users in selecting an appropriate segmentation tool for neuroimages analysis.

Background. Iron deposits impair neurons and cause cognitive disorders through different pathways. Today, with the help of quantitative susceptibility mapping (QSM), iron accumulations in different brain areas can be evaluated and measured.This research aims to identify the changes of iron deposits in the brain nuclei in different stages of cognitive disorders using the QSM technique and introduces biomarkers for the early diagnosis of these disorders. Methods. The data of thirty-five participants with normal cognitive conditions and forty six patients with cognitive disorders, classified into four different groups based on the severity of the disorder, were downloaded from the ADNI database. After determining the regions (ROIs) with the FSL automatic segmentation tool, the mean QSM values of twelve brain nuclei were determined with the help of Slicer software, and SPSS software was used for statistical analysis. Results. There was an inverse correlation in the left putamen nucleus of LMCI group (P=0.041). Direct correlation between age variable and QSM values of the left putamen nucleus (P=0.000078) and right putamen nucleus (P=0.000017) of the control group were reported. Conclusion. As the severity of disorders increases in the LMCI group, the number of paramagnetic deposits, such as iron, in the left putamen nucleus increases. Therefore, the QSM values of the left putamen nucleus can be used as an indicator of the severity of the disorder in the advanced stages of cognitive disorders. Practical Implications. Iron deposits cause damage to neurons and cause cognitive disorders through various pathways. Today, with the help of the quantitative susceptibility mapping technique, the numeral of iron accumulations in different brain areas can be evaluated and measured. This research aims to identify the changes of iron deposits in the brain nuclei in different stages of cognitive disorders by using the Quantitative susceptibility mapping (QSM) technique to introduce biomarkers for early diagnosis of these disorders.

Abstract Background Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system. Since the core cause of many of the symptoms of autism spectrum disorder is due to changes in the structure of the brain, the importance of examining the structural abnormalities of the brain in these disorder becomes apparent. The aim of this study is evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging (sMRI). sMRI images of 26 autistic and 26 Healthy control subjects in the range of 5–10 years are selected from the ABIDE database. For a better assessment of structural abnormalities, the surface and volume features are extracted together from this images. Then, the extracted features from both groups were compared with the sample t test and the features with significant differences between the two groups were identified. Results The results of volume-based features indicate an increase in total brain volume and white matter and a change in white and gray matter volume in brain regions of Hammers atlas in the autism group. In addition, the results of surface-based features indicate an increase in mean and standard deviation of cerebral cortex thickness and changes in cerebral cortex thickness, sulcus depth, surface complexity and gyrification index in the brain regions of the Desikan–Killany cortical atlas. Conclusions Identifying structurally abnormal areas of the brain and examining their relationship to the clinical features of Autism Spectrum Disorder can pave the way for the correct and early detection of this disorder using structural magnetic resonance imaging. It is also possible to design treatment for autistic people based on the abnormal areas of the brain, and to see the effectiveness of the treatment using imaging.

Introduction: Exquisite soft tissue contrast of magnetic resonance images (MRI) and the new combined radiotherapy system of MR-Linac have been the main impetus for applying MR imaging in radiotherapy. One limitation of MR-based radiotherapy is the geometric distortion of MR images that can generate errors in the contouring and dosimetry stages. This study aimed to evaluate and correct geometric distortion for radiotherapy applications. Material and Methods: A large field of view (FOV) phantom develop using Perspex sheets and 325 plastic pipes. The quantification and correction of MR images' system-related geometric distortion are conducted for HASTE protocol by MATLAB and 3D slicer software in phantom and patient images. The effect of MRI images geometrical distortion was evaluated for ten patients undergoing body radiotherapy treatment. CT images were used as a primary dataset to estimate the distortion map. Results: The phantom investigation results indicate that in radial distances of < 13 cm (or FOVs < 25 cm), the amount of distortion is under 2 mm. Still, at more considerable radial distances, distortion may increase up to about 3.5 cm. MR images of Patients with lateral (LAT) and anterior-posterior (AP) diameters of more than 38 cm and 25 cm respectively, need to be corrected for geometric distortion. Conclusion: MR images' geometric precision in large FOVs is not sufficient for MRI only treatment planning of radiotherapy and further corrections are required. The B-spline deformable registration method can correct the MR geometric distortion until an acceptable range of 2 mm for radiotherapy applications.

Abstract Background Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system whose main manifestations are defects in social interactions, communication, repetitive behaviors, and limited interests. Over the years, the use of magnetic resonance imaging (MRI) to help identify patterns that are common in people with autism has increased for classification purposes. This study propose a method for classifying ASD patients versus controls using structural MRI information. In order to increase the accuracy of this method, the volume and surface features of the structural images are used simultaneously. Results The accuracy of diagnosis respectively was 86.29%, 71.15%, 86.53%, and 88.46% with SVM, RF, KNN, and ANN classifiers. The highest accuracy of diagnosis was obtained using ANN. Conclusions Since clinical evaluations for the diagnosis of autism are extremely time-consuming and depend on the expertise of a specialist, the importance of intelligent diagnosis of this disorder becomes clear. The aim of this study was to design an intelligent system to diagnose autism spectrum disorder.

Keypoints 1. In this article, A review of the results of articles on the use of QSM technique to identify nuclei exposed to iron deposition in various neurodegenerative diseases was performed. 2. Brain nuclei with the highest changes in iron deposition were identified as a biomarker for the identification of specific neurological diseases 3. By studying recent articles, The best toolbox for each step of the QSM processing algorithm was introduced.

Abstract The presence of iron is essential for many biological processes in the body. But sometimes, for various reasons, the amount of iron deposition in different areas of the brain increases, which leads to problems related to the nervous system. Quantitative susceptibility mapping (QSM) is one of the newest magnetic resonance imaging (MRI)-based methods for assessing iron accumulation in target areas. This Narrative Review article aims to evaluate the performance of QSM compared to other methods of assessing iron deposition in the clinical field. Based on the results, we introduced related basic definitions, some neurodegenerative diseases, methods of examining iron deposition in these diseases, and their advantages and disadvantages. This article states that the QSM method can be introduced as a new, reliable, and non-invasive technique for clinical evaluations. Graphical abstract

Precise detection of Alzheimer's disease (AD), especially at the early stages, i.e., early mild cognitive impairment (EMCI) and MCI, allows the physicians to promptly intervene to prevent the progression to advanced stages. However, identification of such stages using non-invasive brain imaging techniques like DWI, remains one of the most challenging tasks due to the subtle and mild changes in the brain structures of the subjects. Findings from previous studies suggested that topological organization alterations occur in the DTI-derived structural connectomes in MCI patients. Therefore, for improving diagnosis performance, we presented a connectome-based deep learning architecture based on BrainNet Convolutional neural network (CNN) model. The proposed model automatically extracts hidden topological features from structural networks using specially-designed convolutional filters. Experiments on 360 subjects, including 120 subjects with EMCI, 120 subjects with MCI and, 120 normal controls (NCs), with both T1-weighted MRI and DWI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI), provided the highest binary classification accuracies of 0.96, 0.98, and 0.95 for NC/EMCI, NC/MCI and EMCI/MCI respectively.In addition, we also investigated the effect of different atlas sizes and fiber descriptors as edge weights on the discriminative ability of the classification performance. Experimental results indicate that our approach exhibited superior performance to previous methods and performed effectively without any prior complex feature engineering and regardless the variability of imaging acquisition protocols and medical scanners.Finally, we observed that DTI-based graph representation of brain regions connections preserve important but hidden connectivity pattern information to discriminate between clinical profiles, and our proposed approach could be easily extended to other neurodegenerative and neuropsychiatric diseases.

Background and objectivesThe study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder (ADHD) has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to investigate the brain alterations in patients with ADHD and Typical Development (TD) children and accurately classify ADHD children from TD controls using the graph-theoretical measures obtained from resting-state fMRI (rs-fMRI).Materials and methodsWe investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. Fifty six drug-naive ADHD children (average age 11.86 ± 2.21 years; 49 male) and 56 age matched TD controls (average age 11.51 ± 1.77 years, 44 male) were included in this study. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFE feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) using Peking center data from ADHD-200 database to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD.ResultsAn accuracy of 78.2% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD.ConclusionPattern recognition and graph measures of the brain networks, based on the rs-fMRI data, can efficiently assist in the classification of ADHD children from TD controls.

Introduction: Many cognitive and social deficits in autism are caused by abnormal functional connections between brain networks, which are manifested by impaired integrity of white matter tracts. White matter tracts are like the "highways" of the brain, which allow fast and efficient communication in different areas of the brain. The purpose of this article is to review the results of autism studies that have used diffusion tensor images (DTI). Diffusion tensor images is a neuroimaging technique to examine the integrity of tracts. Conclusion: The results of these studies suggest that neural tracts can be abnormal in people with Autism spectrum disorder (ASD) due to impaired white matter integrity. Thus, changes in these tracts in the brains of people with ASD are helpful in identifying individual differences. Although most studies have reported decreased FA and increased MD, RD, and AD in white matter tracts, some studies have reported increased FA or no significant difference between the control and autistic groups.

Background: With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Objectives: In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM). Participants: For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD. Measure: ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software. Results: The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups. Conclusions: Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.

Recent developments in dental implant have heightened the urgent need to natural tissue adhesives estrogenic materials with ability of promoting the proliferation and osteoblastic differentiation in human dental pulp-derived stem cells, to provide better integration of tissue for dentistry. Up to now, far little attention has been paid to adhesives extract of the root of Ferula sp. which contains biomaterial compounds with estrogenic activities. Prior to undertaking the investigation, analysis of the extract of the root of F. ovina revealed a novel terpenoid, and we identified it as Fenoferin. So far, this paper has focused on Fenoferin compared to Ferutinin and root extract to determine if Fenoferin caused changes in craniofacial cartilage, bone (ceratohyal) and tooth mineralization. Following the purpose of study, we used zebrafish as a well-developed model system for studying bone development, so the developing zebrafish larvae were exposed to various concentration of compounds at 2dpf, and the histological analyses were performed at 6dpf. The result of the current study highlights the importance of F. ovina in studies related to dental regenerative medicine.

Background and ObjectivesFocal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.MethodsMagnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.ResultsNeural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.ConclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.

Introduction: Regular implementation of quality control (QC) program in diagnostic X-ray facilities may affect both image quality and patient radiation dose due to the changes in exposure parameters. Therefore, this study aimed to investigate the status of randomly selected conventional radiographic X-ray devices installed in radiology centers of Great Khorasan Province, Iran, to produce the data needed to formulate QC policies, which are essential to ensure the accuracy of the diagnosis while minimizing the radiation dose. Material and Methods: This cross-sectional study was performed using a calibrated Piranha multi-purpose detector to measure QC parameters in order to unify X‐ray imaging practices using international guidelines. The QC parameters included voltage accuracy, voltage reproducibility, exposure time accuracy, exposure time reproducibility, tube output linearity with time andmilliampere (mA), and tube output reproducibility. Data analysis procedures were performed based on the type of an X-ray generator, which has not been reported in previous studies. Results: The results showed that the implementation of high-frequency X-ray generators were more advantageous compared to alternative current generators, due to their efficient, better accuracy, linearity, and reproducibility. Conclusion: The survey revealed that the QC program was not conducted at regular intervals in some of the investigated radiology centers, mostly because of inadequate enforcement by national regulatory authorities for implementation of QC program.

Introduction: In recent years, the number of computed tomography (CT) scans, which is a high-dose technique, has increased significantly. Head and neck CT is performed frequently and thyroid, particularly in children, has always been considered a sensitive organ. In recent years, radiobiologists and health physicists have been more concerned about the safety of lenses of the eyes, as cataract is no longer considered a deterministic effect. Material and Methods: In the present study, incurred doses to the thyroid and lens of the eye of 140 patients who underwent common head and neck CT at five hospitals were measured by thermoluminescent dosimeters (TLD-100). The patients were divided into two age groups of pediatrics and adults. TLD chips were placed on the patient’s skin surface. For each patient, scan parameters, sex and age were recorded. Exposed TLDs were read by a manual TLD reader. Results: The verage absorbed dose of the thyroid, as well as the lenses of the left and right eyes were 5.89±1.74, 15.84±2.81 and 16.25±2.57, respectively, for the pediatric patients and 5.00±1.17, 17.64±1.69 and 24.41±1.89 for adults. Patient-specific organ doses were influenced by the scanned region, scan protocol and patient's age. Conclusion: In the present study, the mean eye dose was much lower than the 500 mGy threshold recommended by International Commission on Radiological Protection (ICRP) for lens of the eye damage, thus, it appears to be clinically safe. While CT scan remains a crucial tool, further dose reduction can be achieved by controlling different factors affecting patient doses.

Introduction Breast cancer is the second cause of mortality among women. Early detection of it can enhance the chance of survival. Screening systems such as mammography cannot perfectly differentiate between patients and healthy individuals. Computer-aided diagnosis can help physicians make a more accurate diagnosis. Materials and Methods Regarding the importance of separating normal and abnormal cases in screening systems, a hierarchical classification system is defined in this paper. The proposed system is including two Adaptive Boosting (AdaBoost) classifiers, the first classifier separates the candidate images into two groups of normal and abnormal. The second classifier is applied on the abnormal group of the previous stage and divides them into benign and malignant categories. The proposed algorithm is evaluated by applying it on publicly available Mammographic Image Analysis Society (MIAS) dataset. 288 images of the database are used, including 208 normal and 80 abnormal images. 47 images of the abnormal images showed benign lesion and 33 of them had malignant lesion. Results Applying the proposed algorithm on MIAS database indicates its advantage compared to previous methods. A major improvement occurred in the first classification stage. Specificity, sensitivity, and accuracy of the first classifier are obtained as 100%, 95.83%, and 97.91%, respectively. These values are calculated as 75% in the second stage Conclusion A hierarchical classification method for breast cancer detection is developed in this paper. Regarding the importance of separating normal and abnormal cases in screening systems, the first classifier is devoted to separate normal and tumorous cases. Experimental results on available database shown that the performance of this step is adequately high (100% specificity). The second layer is designed to detect tumor type. The accuracy in the second layer is obtained 75%.

Purpose: The purpose of this study was to investigate the relationship between organizational citizenship behavior and job performance among public librarians in Ilam province.Methodology: The methodology applied for this research was descriptive and correlational in nature, with data collected through survey methods. Utilizing the organizational citizenship behavior questionnaire developed by Podsakoff et al. (1990) and the job performance questionnaire by Paterson (1970), the study focused on the librarians in Ilam province in 2023. With three predictor variables considered, a confidence level of 0.95, test power of 0.90, and G*Power software indicating a minimum required sample size of 99 individuals, a total of 103 participants were included in the analysis after accounting for an additional 5% sample volume.Findings: The findings revealed significant relationships between altruism and civil virtue among librarians, as well as associations between respect for librarians and their job performance. However, no significant correlations were found between conscientiousness or chivalry and job performance.Conclusion: In democratic systems, increasing attention is being placed on citizen participation. Active engagement from organizational members as good citizens is crucial for fostering positive behaviors within an organization. Recognizing organizational citizens as valuable resources, their behavior plays a key role in overall success. This study highlights the positive link between organizational citizenship behavior and librarian performance in Ilam province.Value: This study adds to the existing literature by examining the impact of organizational citizenship behavior on librarian job performance and underscores the importance of this relationship in enhancing professional performance. The results can offer valuable insights for cultural officials, public library managers, and policymakers seeking to motivate library staff effectively.