The Design of X-Ray Film Reader with Film Presence Detector

X-Ray viewer is a tool for observing the results of X-Ray films using ray lighting. It aims to get clearer readings of X-Ray films by radiographers and doctors. X-Ray viewers in hospitals generally cannot be carried anywhere because they use fluorescent lamps as a source of radiation and use 220 Volt AC voltage directly. So that its use is less effective and efficient because it must be connected directly to a 220 Volt AC power source and requires large power. In this regard the author wants to design an X-Ray viewer tool that can be used to read the results of X-Ray films clearly and is portable so that the device can be used anywhere because it uses a battery as a voltage source and is equipped with a presence detection sensor film in order to save energy so that the use of tools is more effective and efficient

Intelligent Scoliosis Screening and Diagnosis: A Survey

Scoliosis is a three-dimensional spinal deformity, which may lead to abnormal morphologies, such as thoracic deformity, and pelvic tilt. Severe patients may suffer from nerve damage and urinary abnormalities. At present, the number of scoliosis patients in primary and secondary schools has exceeded five million in China, the incidence rate is about 3% to 5% which is growing every year. The research on scoliosis, therefore, has important clinical value. This paper systematically introduces computer-assisted scoliosis screening and diagnosis as well as analyzes the advantages and limitations of different algorithm models in the current issue field. Moreover, the paper also discusses the current development bottlenecks in this field and looks forward to future development trends.Comment: in Chinese languag

Designing Efficient Deep Learning Models for Computer-Aided Medical Diagnosis

Traditional clinician diagnosis, which requires intensive manual effort from experienced medical doctors and radiologists, is notoriously time-consuming, costly and at times error prone. To alleviate these issues, computer-aided diagnosis systems are often used to improve accuracy in early detection, diagnosis, treatment plan and an outcome prediction. While these systems are making strides, significant challenges remain due the scarcity of publicly available data, high annotation cost, and suboptimal performance in detecting rare targets. In this thesis, we design efficient deep learning models for computer-aided medical diagnosis. The contributions are two-fold: First, we introduce an over-sampling method for learning the inter-class mapping between under-represented class samples and over-represented samples in a bid to generate under-represented class samples using unpaired image-to-image translation. These synthetic images are then used as additional training data in the task of detecting abnormalities (i.e. melanoma, COVID-19). Our method achieves improved performance on a standard skin lesion classification benchmark. We show through feature visualization that our approach leads to context based lesion assessment that can reach an expert dermatologist level. Additional experiments also demonstrate the effectiveness of our model in COVID-19 detection from chest radiography images. The synthetic images not only improve performance of various deep learning architectures when used as additional training data under heavy imbalance conditions, but also detect the target class with high confidence. Second, we present a simple, yet effective end-to-end depthwise encoder-decoder fully convolutional network architecture, dubbed Sharp U-Net, for binary and multi-class biomedical image segmentation. Instead of applying a plain skip connection such as U-Net, a depthwise convolution of the encoder feature map with a sharpening kernel filter is employed prior to merging the encoder and decoder features, thereby producing a sharpened intermediate feature map of the same size as the encoder map. Using this sharpening filter layer, we are able to not only fuse semantically less dissimilar features, but also smooth out artifacts throughout the network layers during the early stages of training. Our extensive experiments on six datasets show that the proposed Sharp U-Net model consistently outperforms or matches the recent state-of-the-art baselines in both binary and multi-class segmentation tasks, while adding no extra learnable parameters