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Purpose: Deep convolutional neural networks are favored methods that are widely used in medical image processing due to their demonstrated performance in this area. Recently, the emergence of new lung diseases, such as COVID-19, and the possibility of early detection of their symptoms from chest computerized tomography images has attracted many researchers to classify diseases by training deep convolutional neural networks on lung computerized tomography images. The trained networks are expected to distinguish between different lung indications in various diseases, especially at the early stages. The purpose of this study is to introduce and assess an efficient deep convolutional neural network, called AFEX-Net, that can classify different lung diseases from chest computerized tomography images. Methods: We designed a lightweight convolutional neural network called AFEX-Net with adaptive feature extraction layers, adaptive pooling layers, and adaptive activation functions. We trained and tested AFEX-Net on a dataset of more than 10,000 chest computerized tomography slices from different lung diseases (CC dataset), using an effective pre-processing method to remove bias. We also applied AFEX-Net to the public COVID-CTset dataset to assess its generalizability. The study was mainly conducted based on data collected over approximately six months during the pandemic outbreak in Afzalipour Hospital, Iran, which is the largest hospital in Southeast Iran. Results: AFEX-Net achieved high accuracy and fast training on both datasets, outperforming several state-of-the-art convolutional neural networks. It has an accuracy of 99.7% and 98.8% on the CC and COVID-CTset datasets, respectively, with a learning speed that is 3 times faster compared to similar methods due to its lightweight structure. AFEX-Net was able to extract distinguishing features and classify chest computerized tomography images, especially at the early stages of lung diseases. Conclusion: The AFEX-Net is a high-performing convolutional neural network for classifying lung diseases from chest CT images. It is efficient, adaptable, and compatible with input data, making it a reliable tool for early detection and diagnosis of lung diseases.
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INTRODUCTION: Electronic health records help collect and communicate patient information among healthcare providers. The confidentiality of information, especially for patients with mental disorders, is paramount due to its profound impacts on individuals' lives' social and personal aspects. This study aimed to investigate the viewpoints and concerns of parents of children with mental disorders regarding the confidentiality and security of their children's information in the Iranian National Electronic Health Record System (IEHRS). METHODS: This is a survey study on parents or guardians of children with mental disorders who visited Kerman's specialised child psychiatry treatment centres. The data collection tool was a researcher-made questionnaire with 28 questions organised in seven sections, including demographic information of parents, children's medical history, Internet use, knowledge about IEHRS, the necessity of data collection, IEHRS security concerns, and privacy concerns. The data were analysed in SPSS 24 software using descriptive statistics and logistic and ordinal regressions to assess the relationship between parents' demographic characteristics and their viewpoints regarding information security and confidentiality concerns. RESULTS: The results showed that more than 85 % of the parents believed that the security of their children's information in IEHRS was moderate to high. More than two-thirds (71 %) of the parents also believed that IEHRS should tighten its privacy policies. Most participants (87 %) were concerned about their children's information security in IEHRS. In this study, the parents' concerns about the privacy and security of information in IEHRS were not significantly associated with their age, gender, or knowledge about IEHRS. CONCLUSIONS: Most parents of children with mental disorders were concerned about the security and confidentiality of their children's information in IEHRS. Thus, health policymakers should maintain a high level of security and establish appropriate privacy and confidentiality rules in IEHRS. In addition, they should be transparent about the system's security mechanisms and confidentiality regulations to win public trust.