This thesis investigates the performance of deep learning models, specifically Resnet50 and TransUnet, in semantic image segmentation on microwell images containing tumor and natural killer (NK) cells. The main goal is to examine the effect of only using bright-field data (1-channel) as input instead of both fluorescent and brightfield data (4-channel); this is interesting since fluorescent imaging can cause damage to the cells being analyzed. The network performance is measured by Intersection over Union (IoU), the networks were trained and using manually annotated data from Onfelt Lab. TransUnet consistently outperformed the Resnet50 for both the 4-channel and 1-channel data. Moreover, the 4-channel input generally resulted in a better IoU compared to using only the bright-field channel. Furthermore, a significant decline in performance is observed when the networks are tested on the control data. For the control data, the overall IoU for the best performing 4-channel model dropped from 86.2\% to 73.9\%. The best performing 1-channel model dropped from 83.8\% to 70.8\% overall IoU.

Previous studies have used convolutional neural networks (CNN) to classify synthetic images created by generative adversarial networks (GANs) to confirm images as either being synthetic or natural. Similar to other research, this thesis will cover the classification of synthetic images witha CNN. However, instead of classifying images created by GANs, a latent diffusion based generator is covered instead. This comparative study gathered results from the performance of botha human baseline as well as a CNN’s ability to classify images generated by stable diffusion and real images created by or taken by humans.The results from this study show that the CNN created greatly outperformed the human baseline when classifying the data sets over multipledifferent image domains.

Diplomová práca sa zaoberá detekciou a hodnotením skreslených snímok v retinálnych obrazových dátach. Teoretická časť obsahuje stručné zhrnutie anatómie oka a metód hodnotenia kvality obrazov všeobecne, ako aj konkrétne hodnotenie retinálnych obrazov. Praktická časť bola vypracovaná v programovacom jazyku Python. Obsahuje predspracovanie dostupných retinálnych obrazov za účelom vytvorenia vhodného datasetu. Ďalej je navrhnutá metóda hodnotenia troch typov šumu v skreslených retinálnych obrazoch, presnejšie pomocou Inception-ResNet-v2 modelu. Táto metóda nebola prijateľná a navrhnutá bola teda iná metóda pozostávajúca z dvoch krokov - klasifikácie typu šumu a následného hodnotenia úrovne daného šumu. Pre klasifikáciu typu šumu bolo využité filtrované Fourierove spektrum a na hodnotenie obrazu boli využité príznaky extrahované pomocou ResNet50, ktoré vstupovali do regresného modelu. Táto metóda bola ďalej rozšírená ešte o krok detekcie zašumených snímok v retinálnych sekvenciách.

Gravel roads connect sparse populations and provide highways for agriculture and the transport of forest goods. Gravel roads are an economical choice where traffic volume is low. In Sweden, 21% of all public roads are state-owned gravel roads, covering over 20,200 km. In addition, there are some 74,000 km of gravel roads and 210,000 km of forest roads that are owned by the private sector. The Swedish Transport Administration (Trafikverket) rates the condition of gravel roads according to the severity of irregularities (e.g. corrugations and potholes), dust, loose gravel, and gravel cross-sections. This assessment is carried out during the summertime when roads are free of snow. One of the essential parameters for gravel road assessment is loose gravel. Loose gravel can cause a tire to slip, leading to a loss of driver control.  Assessment of gravel roads is carried out subjectively by taking images of road sections and adding some textual notes. A cost-effective, intelligent, and objective method for road assessment is lacking. Expensive methods, such as laser profiler trucks, are available and can offer road profiling with high accuracy. These methods are not applied to gravel roads, however, because of the need to maintain cost-efficiency.  In this thesis, we explored the idea that, in addition to machine vision, we could also use machine hearing to classify the condition of gravel roads in relation to loose gravel. Several suitable classical supervised learning and convolutional neural networks (CNN) were tested. When people drive on gravel roads, they can make sense of the road condition by listening to the gravel hitting the bottom of the car. The more we hear gravel hitting the bottom of the car, the more we can sense that there is a lot of loose gravel and, therefore, the road might be in a bad condition. Based on this idea, we hypothesized that machines could also undertake such a classification when trained with labeled sound data. Machines can identify gravel and non-gravel sounds. In this thesis, we used traditional machine learning algorithms, such as support vector machines (SVM), decision trees, and ensemble classification methods. We also explored CNN for classifying spectrograms of audio sounds and images in gravel roads. Both supervised learning and CNN were used, and results were compared for this study. In classical algorithms, when compared with other classifiers, ensemble bagged tree (EBT)-based classifiers performed best for classifying gravel and non-gravel sounds. EBT performance is also useful in reducing the misclassification of non-gravel sounds. The use of CNN also showed a 97.91% accuracy rate. Using CNN makes the classification process more intuitive because the network architecture takes responsibility for selecting the relevant training features. Furthermore, the classification results can be visualized on road maps, which can help road monitoring agencies assess road conditions and schedule maintenance activities for a particular road. / <p>Due to unforeseen circumstances the seminar was postponed from May 7 to 28, as duly stated in the new posting page.</p>

Machine learning and AI are growing rapidly and they are being implemented more often than before due to their high accuracy and performance. One of the biggest challenges to machine learning is data collection. The training data is the most important part of any machine learning project since it determines how the trained model will behave. In the case of object classification and detection, capturing a large number of images per object is not always possible and can be a very time-consuming and tedious process. This thesis explores options specific to image classification that help reducing the need to capture many images per object while still keeping the same performance accuracy. In this thesis, experiments have been performed with the goal of achieving a high classification accuracy with a limited dataset. One method that is explored is to create artificial training images using a game engine. Ways to expand a small dataset such as different data augmentation methods, and regularization methods, are also employed. / Maskininlärning och AI växer snabbt och de implementeras allt oftare på grund av deras höga noggrannhet och prestanda. En av de största utmaningarna för maskininlärning är datainsamling. Träningsdata är den viktigaste delen av ett maskininlärningsprojekt eftersom den avgör hur den tränade modellen kommer att bete sig. När det gäller objektklassificering och detektering är det inte alltid möjligt att ta många bilder per objekt och det kan vara en process som kräver mycket tid och arbete. Det här examensarbetet utforskar alternativ som är specifika för bildklassificering som minskar behovet av att ta många bilder per objekt samtidigt som prestanda bibehålls. I det här examensarbetet, flera experiment har utförts med målet att uppnå en hög klassificeringsprestanda med en begränsad dataset. En metod som utforskas är att skapa träningsbilder med hjälp av en spelmotor. Metoder för att utöka antal bilder i ett litet dataset, som data augmenteringsmetoder och regleringsmetoder, används också.

This study explores the potential of artificial intelligence (AI) in enhancing the diagnosis of brain tumours, specifically through a comparative analysis of two advanced deep learning (DL) models, ResNet50 and YOLOv8, applied to detect and classify brain tumours in MRI images. The study addresses the critical need for rapid and accurate diagnostic tools in the medical field, given the complexity and diversity of brain tumours. The research was motivated by the potential benefits AI could offer to medical diagnostics, particularly in terms of speed and accuracy, which are crucial for effective patient treatment and outcomes. The performance of the ResNet50 and YOLOv8 models was evaluated on a dataset of 7023 MRI images across four tumour types. Key metrics used were accuracy, precision, recall, specificity, F1-score, and processing time, to identify which model performs better in detecting and classifying brain tumours. The findings demonstrates that although both models exhibit high performance, YOLOv8 surpasses ResNet50 in most metrics, particularly showing advantages in speed. The findings highlight the effectiveness advanced DL models in medical image analysis, providing a significant advancement in brain tumour diagnosis. By offering a thorough comparative analysis of two commonly used DL models, aligning with ongoing approaches to integrate AI into practical medical application, and highlighting their potential uses, this study advances the area of medical AI providing insight into the knowledge required for the deployment of future AI diagnostic tools.