Lung cancer detection based on computed tomography image using convolutional neural networks.

BACKGROUND: Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages. OBJECTIVE: Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs). METHODS: The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control. RESULTS: The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model's highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984. CONCLUSION: The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.

Perceived Need for Mental Health Services Among Healthcare Workers During the Coronavirus Disease-19 Pandemic in Turkey: A Multicenter Cross-sectional Study

Background: The coronavirus disease-2019 pandemic has contributed to work-related psychosocial risks in healthcare workers. Aims: To evaluate the perceived need for mental health services and related factors in Turkish healthcare workers practicing in pandemic hospitals. Study Design: Cross-sectional study. Methods: Data were collected from face-to-face interviews with healthcare workers at 19 pandemic hospitals in 13 provinces between September and November 2021. The study survey included the evaluation of the perceived need for and utilization of mental health services in the previous year, as well as sociodemographic, health-related, and work-related characteristics, the General Health Questionnaire-12, the World Health Organization Quality of Life-BREF (WHOQoL-BREF) questionnaire, and the Fear of coronavirus disease-2019 scale (FCV-19S). Results: Of 1,556 participants, 522 (33.5%) reported a perceived need for mental health services, but only 133 (8.5%) reported receiving these services. Multiple logistic regression analysis of the perceived need for mental health services revealed significant relationships with lower age, female sex, being a current smoker, having a chronic disease, having a mental disorder, coronavirus disease-2019 contact within the last three months in settings other than the home or workplace, a positive coronavirus disease-2019 vaccination history, being a physician, being a non-physician healthcare professional, and coronavirus disease-2019 contact within the last three months at work. After adjustment for these characteristics, higher General Health Questionnaire-12 and FCV-19S scores and lower WHOQoL-BREF domain scores were related to the perceived need for mental health services in logistic regression analyses. Conclusion: The findings indicate a substantial need for mental health services amongst Turkish healthcare workers during the pandemic and outline participants' characteristics regarding high-priority groups for the intervention. Future research may focus on developing actions and evaluating their efficiency.