Detecting COVID-19 in chest X-ray images

One reliable way of detecting coronavirus disease 2019 (COVID-19) is using a chest x-ray image due to its complications in the lung parenchyma. This paper proposes a solution for COVID-19 detection in chest x-ray images based on a convolutional neural network (CNN). This CNN-based solution is developed using a modified InceptionV3 as a backbone architecture. Self-attention layers are inserted to modify the backbone such that the number of trainable parameters is reduced and meaningful areas of COVID-19 in chest x-ray images are focused on a training process. The proposed CNN architecture is then learned to construct a model to classify COVID-19 cases from non-COVID-19 cases. It achieves sensitivity, specificity, and accuracy values of 93%, 96%, and 96%, respectively. The model is also further validated on the so-called other normal and abnormal, which are non-COVID-19 cases. Cases of other normal contain chest x-ray images of elderly patients with minimal fibrosis and spondylosis of the spine, whereas other abnormal cases contain chest x-ray images of tuberculosis, pneumonia, and pulmonary edema. The proposed solution could correctly classify them as non-COVID-19 with 92% accuracy. This is a practical scenario where non-COVID-19 cases could cover more than just a normal condition

Automatic segmentation of kidney and liver tumors in CT images

Automatic segmentation of hepatic lesions in computed tomography (CT) images is a challenging task to perform due to heterogeneous, diffusive shape of tumors and complex background. To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer vision tasks, including medical image processing. In this technical report, we carry out research focused on more careful approach to the process of learning rather than on complex architecture of the CNN. We have chosen MICCAI 2017 LiTS dataset for training process and the public 3DIRCADb dataset for validation of our method. The proposed algorithm reached DICE score 78.8% on the 3DIRCADb dataset. The described method was then applied to the 2019 Kidney Tumor Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38% for kidney and 67.38% for tumor Dice scores

Simulated Surgical Model Design for Myringotomy and Tympanostomy Tube Insertion in Children using Medical Image Processing and 3D-Printing Technologies

Objective: Researchers aimed to design surgical simulation models using medical image processing and 3D-printing technologies to train otolaryngologie residents with correct surgical techniques and study their skills improvement. Materials and Methods: The models were produced for three age ranges (group A: 8-12 years old, group B: 3-7 years old, and group C: 10 months - 2 years old). Eleven residents were practiced from older to younger child models. Overall surgical time and results were evaluated to determine improvement. Both residents and specialists assessed satisfaction surveys after training. Results: The median operational time was significantly reduced by 64.57% in model A and 50.24% in model B (p < 0.05). Operating time and surgical skills improved in order from models A, B, and C. Model C showed the most improvement with correct operational techniques in myringotomy incision (66.7%, p = 0.003) and tympanostomy tube insertion (48.5%, p = 0.011). Residents’ and specialists’ satisfaction assessments exhibited prominent satisfaction results with surgical simulation model training. Conclusion: Surgical simulation models training enhanced residencies’ confidence and improved correct surgical techniques. Residencies can gradually practice skills from fundamental to more complicated techniques in younger child model where symptom occurs

Classification of chest radiography from general radiography using deep learning approach

Classifying x-ray images into individual classes of body parts is needed, when they are mixed without proper labels. This paper proposes a hierarchical training of convolutional neural network (CNN)-based framework, for classifying chest posterior–anterior (PA) x-ray images from other 12 classes. The first model is constructed for filtering chest PA from the other classes, before constructing the second model to separate the rest of the 12 classes. This is beneficial to address class-imbalanced and overfitting problems, with assists of class weighting and data augmentation. The proposed method achieves promising performances with precision and recall of 100% and F0.5of 99%