Edge computing based real-time Nephrops (Nephrops norvegicus) catch estimation in demersal trawls using object detection models.

In demersal trawl fisheries, the unavailability of the catch information until the end of the catching process is a drawback, leading to seabed impacts, bycatches and reducing the economic performance of the fisheries. The emergence of in-trawl cameras to observe catches in real-time can provide such information. This data needs to be processed in real-time to determine the catch compositions and rates, eventually improving sustainability and economic performance of the fisheries. In this study, a real-time underwater video processing system counting the Nephrops individuals entering the trawl has been developed using object detection and tracking methods on an edge device (NVIDIA Jetson AGX Orin). Seven state-of-the-art YOLO models were tested to discover the appropriate training settings and YOLO model. To achieve real-time processing and accurate counting simultaneously, four frame skipping ideas were evaluated. It has been shown that adaptive frame skipping approach, together with YOLOv8s model, can increase the processing speed up to 97.47 FPS while achieving correct count rate and F-score of 82.57% and 0.86, respectively. In conclusion, this system can improve the sustainability of the Nephrops directed trawl fishery by providing catch information in real-time.

COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks.

X-ray is a useful imaging modality widely utilized for diagnosing COVID-19 virus that infected a high number of people all around the world. The manual examination of these X-ray images may cause problems especially when there is lack of medical staff. Usage of deep learning models is known to be helpful for automated diagnosis of COVID-19 from the X-ray images. However, the widely used convolutional neural network architectures typically have many layers causing them to be computationally expensive. To address these problems, this study aims to design a lightweight differential diagnosis model based on convolutional neural networks. The proposed model is designed to classify the X-ray images belonging to one of the four classes that are Healthy, COVID-19, viral pneumonia, and bacterial pneumonia. To evaluate the model performance, accuracy, precision, recall, and F1-Score were calculated. The performance of the proposed model was compared with those obtained by applying transfer learning to the widely used convolutional neural network models. The results showed that the proposed model with low number of computational layers outperforms the pre-trained benchmark models, achieving an accuracy value of 89.89% while the best pre-trained model (Efficient-Net B2) achieved accuracy of 85.7%. In conclusion, the proposed lightweight model achieved the best overall result in classifying lung diseases allowing it to be used on devices with limited computational power. On the other hand, all the models showed a poor precision on viral pneumonia class and confusion in distinguishing it from bacterial pneumonia class, thus a decrease in the overall accuracy.

An analytical method to create patient-specific deformed bone models using X-ray images and a healthy bone model.

Generation of patient-specific bone models from X-ray images is useful for various medical applications such as total hip replacement, implant manufacturing, knee kinematic studies and deformity correction. These models may provide valuable information required for a more reliable operation. In this work, we propose a new algorithm for generating patient-specific 3D models of femur and tibia with deformity, using only a generic healthy bone model and some simple measurements taken on the X-ray images of the diseased bone. Using the X-ray measurements, an interpolation function (a polynomial or a cubic spline) is fit to the mid-diaphyseal curve of the actual bone and the generic bone model is deformed in the guidance of this function with free form deformation method. The created models are intended to be used mainly for the visualization of fixation procedure in software-supported external fixation systems. An error measure is defined to quantify the error in this matching procedure. The method is found to be capable of producing deformed tibia models that satisfactorily reflect the actual bones, as confirmed by two orthopaedic surgeons who use software-supported external fixation systems regularly.