Mona Sarhadi, Mohammad Amin Shayegan,
Volume 15, Issue 1 (Apr & May 2021)
Abstract
Background and Aim: For effective treatment of Alzheimer's disease (AD), it is important to accurately diagnosis of AD and its earlier stage, Mild Cognitive Impairment (MCI). One of the most important approaches of early detection of AD is to measure atrophy, which uses various kinds of brain scans, such as MRI. The main objective of the current research was to provide a computerized diagnostic system for early diagnosis of AD, using leraning machine algorithms, to help physicians. The proposed system diagnoses AD by examining the hippocampal atrophy of brain MRI images and increases the accuracy of the diagnosis.
Materials and Methods: In this study, hippocampus was segmented from the other parts of the brain by using active contour and convolutional neural network and then, three groups of “Normal Controls: NC”, AD and MCI were classified by using the SVM classifier.
Results: The proposed method has succeeded in classifying AD against NC with 98.77%, 98.74% and 97.96% in average for accuracy, sensitivity and specificity, respectively. Also in classification of MCI against NC, the mean accuracy, sensitivity and specificity of 96.14%, 96.23% and 88.21% were achieved, respectively. Compared with the nearest rival method, the proposed method showed improvement accuracy and sensitivity of classification AD from NC with 1.64% and 2.81% respectively. Also, in classification of MCI from NC it showed improvement for accuracy with 8.9% and sensitivity with 2.16%, respectively. Improving in results were due to the use of a modified ACM segmentation algorithm, the use of a combination of features extracted from hippocampal images and features already created by the ImageNet network, the removal of inappropriate features from the feature vector, and the use of deep Inception v3 network.
Concolusion: Based on the results, the combination of polygon surrounding the hippocampus features and deep network features can be useful for detection of AD and MCI.
Omid Ali Gholami, Jamil Sadeghifar, Bahareh Kabiri, Shabnam Ghasemyani, Sadegh Sarhadi, Reza Jorvand,
Volume 17, Issue 4 (10-2023)
Abstract
Background and Aim: Health literacy is recognized as a key determinant of health and is a central focus of public health policy strategies. The present study aimed to assess the health literacy level and identify the factors influencing it among the clients of comprehensive health service centers in Ilam city.
Materials and Methods: In 2022, a descriptive-analytical study was conducted to examine 429 clients aged 18-65 years who visited comprehensive selected health service centers in Ilam city. For adults the data collection tool used was the Helia health literacy questionnaire. Sampling was conducted in nine clusters, with each cluster consisting of 50 samples. The data was analyzed using SPSS software, which included descriptive statistical tests, Pearson’s correlation coefficient, and one-way analysis of variance, with a significance level set at 0.05.
Results: Based on the results, the average health literacy score was 80.16 ± 16.50. In terms of health literacy, 18.97% of people had inadequate or not very adequate health literacy, while 44.39% of the participants had excellent health literacy. The average health literacy scores across different dimensions are as follows: access to health information 65.74, comprehension of information 81/81, reading information skills 12.74, evaluation of information 05.75, and decision making and behavior based on information 61.92. A significant relationship was observed between job, education, and income variables and various dimensions of average health literacy (p-value≤0.001). However, there was no significant relationship between general health literacy and age, gender, and place of residence (P≤0.05).
Conclusion: The results of the present study demonstrate that vulnerable groups have significantly lower literacy levels. Furthermore, given the impact of education on enhancing people’s health literacy, it is advisable to leverage mass media, social networks, and educational centers to enhance literacy levels as a potential factor in community health.
