The rising incidence of skin cancer necessitates the development of automated, reliable diagnostic tools to support clinicians. While deep learning models, particularly from the YOLO family, have shown promise, their application in real-world scenarios is limited by challenges such as class imbalance and the inability to process images of healthy skin, leading to potential false positives. This study presents a comprehensive comparative analysis of 32 object detection models, primarily from the YOLO architecture (v5–v12) and RT-DETR, to identify the most effective solution for skin lesion detection. We curated a novel, balanced dataset of 10,000 images based on the ISIC archive, comprising 10 distinct lesion classes (benign and malignant). Crucially, we introduced a dedicated ‘background’ class containing 1000 images of clear skin, a novelty designed to enhance model robustness in clinical practice. Models were systematically evaluated and filtered based on performance metrics (mAP, Recall) and complexity. Through a multi-stage evaluation, the YOLOv9c model was identified as the superior architecture, achieving a mAP@50 of 72.5% and a Recall of 71.6% across all classes. The model demonstrated strong performance, especially considering the dataset’s complexity with 10 classes and background images. Our research establishes a new benchmark for skin lesion detection. We demonstrate that including a ‘background’ class is a critical step towards creating clinically viable tools. The YOLOv9c model emerges as a powerful and efficient solution. To foster further research, our curated 10-class dataset with background images will be made publicly available.

There are numerous benefits associated with creating digital copies of anatomical structures, which can be used during patient diagnosis. Such models can be used not only for visualization, but also in order to assess the condition of the patient. As advances in both medical imaging and 3D graphics are made, it is necessary to determine areas of application of the known reconstruction algorithms. Specifically, it is crucial to find advantages and disadvantages of known approaches to mesh generation, depending on the properties of the object and compare the quality of their results. In order to provide reliable ground-truth data, three 3D models with features resembling those identified in anatomical structures have been created. Based on these meshes, sets of CT-like DICOM images have been generated. Five different reconstruction approaches were proposed: using 3D occupancy information directly, two ways of obtaining point clouds and two methods that utilize Signed Distance Field. A neural network architecture for the SDF upsampling has also been presented. The obtained results justify the popularity of the Marching Cubes algorithm, as it produced accurate reconstructions most reliably. However, for certain scenarios, promising alternatives have been found. The presented outcomes make it clear that the approach to reconstruction must be tailored to the specific problem.

The aim of this study was to develop an effective and fast tool to support the automatic segmentation of skin lesions, with particular emphasis on the precise differentiation between malignant and benign lesions. In response to the problem of high false positive rates in existing CAD systems, modern neural network architectures from the YOLO family (YOLOv8, YOLOv9, YOLOv11, YOLOv12, and YOLOv26) were used in this research. The models were trained and evaluated on a new, balanced dataset (7000 images) based on the ISIC archive, where the key innovation was the introduction of a dedicated background class representing healthy skin. Through a multi-stage, rigorous optimization process, it was demonstrated that the yolov11s-seg model is highly effective for this task. It achieved a strong balance between effectiveness and processing speed, obtaining an mAP@50 score of 0.840 and an overall precision of 0.852. From a clinical perspective, the model’s high sensitivity (85.9%) in detecting the most aggressive lesion, invasive melanoma (MI), is particularly noteworthy. Thanks to its extremely short inference time (only 4.8 ms), the proposed yolov11s-seg variant overcomes the limitations of heavy hybrid architecture, providing a stable and highly efficient solution showing significant potential for deployment in real-time medical mobile applications.