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Abstract
Capsule endoscopy has emerged as a non-invasive alternative to traditional gastrointestinal inspection procedures, such as endoscopy and colonoscopy. Removing sedation risks, it is a patient-friendly and hospital-free procedure, which allows small bowel assessment, region not easily accessible by traditional methods. Recently, deep learning techniques have been employed to analyse capsule endoscopy images, with a focus on lesion classification and/or capsule location along the gastrointestinal tract. This research work presents a novel approach for testing the generalization capacity of deep learning techniques in the lesion location identification process using capsule endoscopy images. To achieve that, AlexNet, InceptionV3 and ResNet-152 architectures were trained exclusively in normal frames and later tested in lesion frames. Frames were sourced from KID and Kvasir-Capsule open-source datasets. Both RGB and grayscale representations were evaluated, and experiments with complete images and patches were made. Results show that the generalization capacity on lesion location of models is not so strong as their capacity for normal frame location, with colon being the most difficult organ to identify.

Abstract
Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random. Currently, the literature offers various algorithms for imputing missing data, each with advantages tailored to specific mechanisms and levels of missingness. This paper introduces a novel approach to missing data imputation using the well-established label propagation algorithm, named Label Propagation for Missing Data Imputation (LPMD). The method combines, weighs, and propagates known feature values to impute missing data. Experiments on benchmark datasets highlight its effectiveness across various missing data scenarios, demonstrating more stable results compared to baseline methods under different missingness mechanisms and levels. The algorithms were evaluated based on processing time, imputation quality (measured by mean absolute error), and impact on classification performance. A variant of the algorithm (LPMD2) generally achieved the fastest processing time compared to other five imputation algorithms from the literature, with speed-ups ranging from 0.7 to 23 times. The results of LPMD were also stable regarding the mean absolute error of the imputed values compared to their original counterparts, for different missing data mechanisms and rates of missing values. In real applications, missingness can behave according to different and unknown mechanisms, so an imputation algorithm that behaves stably for different mechanisms is advantageous. The results regarding ML models produced using the imputed datasets were also comparable to the baselines.