Computed Radiography exams are rarely performed by the same physicians who will interpret the image. Therefore, if the image does not help the physician diagnose the patient, the image can be rejected by the interpreting physician. The rejection normally happens after the patient has already left the hospital meaning that they will have to return to retake the exam. This leads to unnecessary work for the physicians and for the patient. In order to solve this problem we have explored deep learning algorithms to automatically analyze the images and distinguish between usable and unusable images. The deep learning algorithms include convolutional neural networks, vision transformers and fusion networks utilizing different types of data. In total, seven architectures were used to train 42 models. The models were trained on a dataset of 61 127 DICOM files containing images and metadata collected from a clinical setting and labeled based on if the images were deemed usable in the clinical setting. The complete dataset was used for training generalized models and subsets containing specific body parts were used for training specialized models. Three architectures were used for classification using images only, where two architectures used a ResNet-50 backbone and one architecture used a ViT-B/16 backbone. These architectures created 15 specialized models and three generalized models. Four architectures implementing joint fusion created 20 specialized models and four generalized models. Two of these architectures had a backbone of ResNet-50 and the other two utilized a ViT-B/16 backbone. For each of the backbones used, two types of joint fusion were implemented, type I and type II, which had different structures. The two modalities utilized were images and metadata from the DICOM files. The best image only model had a ViT-B/16 backbone and was trained on a specialized dataset containing hands and feet. This model reached an AUC score of 0.842 and MCC of 0.545. The two fusion models trained on the same dataset reached an AUC score of 0.843 and 0.834 respectively and an MCC of 0.547 and 0.546 respectively. We concluded that it is possible to perform automatic rejections with deep learning models even though the results of this study are not good enough for clinical use. The models using ViT-B/16 performed better than the ones using ResNet-50 for all models. The generalized and specialized models performed equally well in most cases with the exception of the smaller subsets of the full dataset. Utilizing metadata from the DICOM files did not improve the models compared to the image only models.

As an important indicator of biodiversity and ecological environment in a region, the number and distribution of animals has been given more and more attention by agencies such as nature reserves, wetland parks, and animal protection supervision departments. To protect biodiversity, we need to be able to detect and track the movement of animals to understand which animals are visiting the space. This thesis uses the improved You Only Look Once Version 5 (YOLOv5) target detection algorithm and Simple online and real-time tracking with a deep association metric (DeepSORT) tracking algorithm to provide technical support for bird monitoring, identification and tracking. Specifically, the thesis tries different improvement methods based on YOLOv5 to solve the problem that small targets in images are difficult to detect. In the backbone network, different attention modules are added to enhance the network feature extraction ability; in the neck network part, the Bi-Directional Feature Pyramid Network (BiFPN) structure is used to replace the Path Aggregation Network (PAN) structure to strengthen the utilization of underlying features; in the detection head part, a high-resolution detection head is added to improve the detection ability of tiny targets. In addition, a better loss function has been used to improve the algorithm’s performance on small birds. The improved algorithms in this paper have been used in multiple comparative experiments on the VisDrone data set and a data set of bird flight images, and the results show that compared with the baseline using YOLOv5, for VisDrone data set, Spatial-to-Depth (SPD)-Convolutional stride-free (Conv) gets the highest training mean Average Precision (mAP) of all methods with an increase from 0.325 to 0.419; for the bird data set, the best result of training mAP that could be achieved is adding a P2 layer, which reaches an improvement from 0.701 to 0.724. After combining the You Only Look Once (YOLO) with DeepSORT to implement the tracking function, the improved method makes the final tracking effect better. / Som en viktig indikator på biologisk mångfald och ekologisk miljö i en region har antal och utbredning av djur uppmärksammats mer och mer av organisationer som som naturreservat, våtmarksparker och djurskyddsmyndigheter. För att skydda den biologiska mångfalden måste vi kunna upptäcka och spåra djurs rörelser för att förstå vilka djur som besöker ett område. Uppsatsen använder den förbättrade YOLOv5-måldetektionsalgoritmen och DeepSORT-spårningsalgoritmen för fågelövervakning, identifiering och spårning. Specifikt undersöks olika förbättringsmetoder baserade på YOLOv5 för att lösa problemet med att små mål i bilder är svåra att upptäcka. I den första delen av nätverket läggs olika uppmärksamhetsmoduler till; i nästa används BiFPN-strukturen för att ersätta PAN-strukturen; i detektionsdelen läggs ett högupplöst detektionshuvud till för att förbättra detekteringsförmågan för små föremål. Dessutom har en bättre förlustfunktion använts för att förbättra algoritmens prestanda för små fåglar och andra djur. De förbättrade algoritmerna har testats flera jämförande experiment på VisDronedatamängden och en datamängd av bilder av flygande fåglar. Resultaten visar att jämfört med baslinjen med YOLOv5s, för VisDrone-datamängden får SPD-Conv det högsta tränings-mAP med en ökning från 0,325 till 0,419; för fågeldatamängden nås det bästa resultatet genom att lägga till ett P2-lager, vilket ger en förbättring från 0,701 till 0,724 av mAP. Efter att ha kombinerat YOLO med DeepSORT för att implementera spårningsfunktionen, blir den slutliga spårningseffekten bättre.