Deep Learning-Based Computer-Aided Pneumothorax Detection Using Chest X-ray Images.

Pneumothorax is a thoracic disease leading to failure of the respiratory system, cardiac arrest, or in extreme cases, death. Chest X-ray (CXR) imaging is the primary diagnostic imaging technique for the diagnosis of pneumothorax. A computerized diagnosis system can detect pneumothorax in chest radiographic images, which provide substantial benefits in disease diagnosis. In the present work, a deep learning neural network model is proposed to detect the regions of pneumothoraces in the chest X-ray images. The model incorporates a Mask Regional Convolutional Neural Network (Mask RCNN) framework and transfer learning with ResNet101 as a backbone feature pyramid network (FPN). The proposed model was trained on a pneumothorax dataset prepared by the Society for Imaging Informatics in Medicine in association with American college of Radiology (SIIM-ACR). The present work compares the operation of the proposed MRCNN model based on ResNet101 as an FPN with the conventional model based on ResNet50 as an FPN. The proposed model had lower class loss, bounding box loss, and mask loss as compared to the conventional model based on ResNet50 as an FPN. Both models were simulated with a learning rate of 0.0004 and 0.0006 with 10 and 12 epochs, respectively.

Deep Neural Networks for Medical Image Segmentation.

Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks. This significant growth is due to the achievements and high performance of the deep learning strategies. This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks. The paper examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks. In comparison to the existing review and survey papers, the present work also discusses the various challenges in the field of segmentation of medical images and different state-of-the-art solutions available in the literature.

Strategy to prevent infection from Covid-19 among security officers of tertiary care centre: A preexperimental study.

BACKGROUND: The extensive spread of Covid-19 pandemic globally became the main cause of concern for everyone, including security officers working in a health care setting. OBJECTIVE: To assess the effectiveness of instructional module for Covid-19 prevention among hospital security officers. METHODS AND MATERIALS: A preexperimental study was conducted at a tertiary care hospital from North India. A total of 344 security officers were selected by the convenient sampling technique. A self-structured knowledge and practice questionnaires and instructional module were developed based on the guidelines released by World Health Organization, Centre for Disease Control and Prevention and Ministry of Health and Family Welfare. Knowledge and practice were pretested, followed by the implementation of a video cum discussion instructional module for Covid-19 prevention. A posttest of knowledge and practice assessment was done after 7 days by using the same questionnaire. Descriptive and inferential statistics were used to compute and analyse the data. RESULTS: The mean age of participants was 29.5 ± 2.25; mos participants (75%) were male security officers. Knowledge and practice scores improved after the implementation of instructional module as mean scores of pretest to mean posttest scores had shown a significant difference (P = 0.00). In practice, instructional module was significantly effective, except for touching hair again and again, as it could be a source of covid-19 infection. CONCLUSION: This study finding highlights the significance of training security officers about the prevention of Covid-19.