## 1 Introduction
Finally, a feature balancing module is implemented followed by the feature fusion module that is introduced between the encoder and decoder to extract image features with different granularities. The developed segmentation technique was tested on the DRIVE and STARE datasets. It was demonstrated that the obtained accuracies (96.11% and 96.62% for DRIVE and STARE, respectively) and sensitivities (86.13% and 78.87%, respectively) outperform other state-of-the-art methods presented in the literature. A computer tool dedicated to the comprehensive analysis of lung changes in computed tomography (CT) images is described in [6]. The proposed system enables the analysis of the correlation between the radiation dose delivered during radiotherapy and the density changes in lungs caused by the fibrosis. The input data, including patient dose, are extracted from the CT images coded in DICOM format. The convolution neural networks are used for CT processing. Next, the selected slices are segmented and registered by the developed algorithms. The results of the analysis are visualized graphically, enabling, for example, the presentation of dose distribution maps in the lungs. It is expected that, thanks to the developed application, it will be possible to demonstrate the statistically significant impact of low doses on lung function for a large number of patients. Some areas of computer-aided diagnosis benefit from worldwide competitions for research teams mainly due to the access to extensive and well-annotated datasets. The study of [PERSON] and [PERSON] [7] addresses the detection of five subtypes of intracranial hemorrhage (ICH) in head CT. The authors trained and validated their tools using a public database provided by the Radiological Society of North America for the international ICH detection competition. The set used in [7] consists of over 372k images from ten thousand patients. Five separate models of a dedicated double-branch architecture were trained to detect every ICH subtype. Each ResNet-50-based branch analyzes two artificial RGB images: one reflecting the original CT slice in three different intensity windows and the other combining the image with its neighbors in the series. The authors report the results produced by each separate branch, but the best results came from another framework. Features extracted by ResNet-50 cores are concatenated and passed to a classifier: either an SVM or a random forest. Depending on the ICH type, the F1 score reaches from 75.3 to 96.6%, surpassing other studies in the field thus far. The ResNet-50 architecture is also the core of a method proposed by [PERSON] et al. [8] for the automated detection of cephalometric landmarks in head X-ray images. Again, the study takes advantage of a public database for an international ISBI Grand Challenge in dental X-ray image analysis. However, the authors support the investigation with their own dataset this time. The analysis is performed within patches extracted from the X-ray image at a registration-based coarse landmark localization stage. The ResNet-50 architecture is replicated and trained nineteen times to address each of nineteen defined landmarks separately. The fully connected layer works in a regression mode to estimate the x and y coordinates of the landmark with a two-argument mean squared error (MSE) loss function. The authors assess their methodology using a radial detection error and a successful detection rate with an assumed tolerance. Despite a relatively small training dataset, the method can detect most landmarks accurately, especially in the ISBI dataset images. The comparison with some state-of-the-art methods favors the current study in most evaluation metrics. The authors indicate that their approach still has room for improvement in terms of computational time, mainly in the initial automated registration. [PERSON] et al. [9] applied deep learning models to segment laboratory rodents in thermal images.