Finding matches between puzzle pieces is a difficult problem relevant to applications that involve restoring broken objects. The main difficulty comes from the similarity of the puzzle pieces and the very small difference between a pair of pieces that almost match and one that does. The proposed solution is based on deep learning models…
Objective: Evaluating the possibility of predicting chronic rhinosinusitis with nasal polyps (CRSwNP) disease course using Artificial Intelligence. Methods: We prospectively included patients undergoing first endoscopic sinus surgery (ESS) for nasal polyposis. Preoperative (demographic data, blood eosinophiles, endoscopy, Lund-Mackay, SNOT-22 and depression PHQ scores) and follow-up data was standardly collected. Outcome measures included SNOT-22, PHQ-9 and…
Detecting icons in Graphical User Interfaces (GUIs) is essential for effective application automation. This study examines the impact of different annotation methods on the performance of object detection models for icon detection in GUIs. We compared manual, automated, and hybrid annotations using three models: Faster R-CNN, YOLOv8, and YOLOv9. The results show that manual…
Decision trees rank among the most popular and efficient classification methods. They are used to represent rules for recursively partitioning the data space into regions from which reliable predictions regarding classes can be made. These regions are usually delimited by axis-parallel or oblique hyperplanes. Axis-parallel hyperplanes are intuitively appealing and have been widely studied.…
Classification and feature selection are two of the most intertwined problems in machine learning. Decision trees (DTs) are straightforward models that address these problems offering also the advantage of explainability. However, solutions that are based on them are either tailored for the problem they solve or their performance is dependent on the split criterion…
Brain disorders are increasingly prevalent today, making accurate brain segmentation essential for effective treatment andrecovery. This paperintroducesanenhancedunsupervisedgraph-basedbrainsegmentationmethod that employs an ellipsoid to select the nodes forming the graph. The method was rigorously evaluated on T1 and T2 modalities using four diverse datasets: the complete NFBS dataset, 48 MRIs from the IXI dataset, 16 images…
Background: Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. Method: The first…
According to the World Health Organization, breast cancer becomes fatal only if it spreads throughout the body. Therefore, regular screening is essential. Whilst mammography is the most frequently used technique, its interpretation can be challenging and time-consuming. For this reason, computer-aided detection and diagnosis systems are increasingly being used for second opinion. However, in…
Web technology advances faster than humans can adapt to it and develop the proper online skills. Most users are not experienced enough to have a good online knowledge on how to protect their data. Thus, many people can become vulnerable to threats. The most common online attacks are through malicious web links, which can…
This paper introduces the UICVD Dataset, a novel resource fostering advancements in Robotic Process Au-tomation (RPA) and Computer Vision. The paper focuses on recognizing UI (User Interface) components of a web application which is not as well known as recognizing real objects in images in the field of computer vision. This dataset derives from…