This paper introduces an advanced method for lung cancer subtype classification and detection using the latest version of YOLO, tailored for the analysis of CT images. Given the increasing mortality rates associated with lung cancer, early and accurate diagnosis is crucial for effective treatment planning. The proposed method employs single-shot object detection to precisely identify and classify various types of lung cancer, including Squamous Cell Carcinoma (SCC), Adenocarcinoma (ADC), and Small Cell Carcinoma (SCLC). A publicly available dataset was utilized to evaluate the performance of YOLOv8. Experimental outcomes underscore the system’s effectiveness, achieving an impressive mean Average Precision (mAP) of 97.1%. The system demonstrates the capability to accurately identify and categorize diverse lung cancer subtypes with a high degree of accuracy. For instance, the YOLOv8 Small model outperforms others with a precision of 96.1% and a detection speed of 0.22 seconds, surpassing other object detection models based on two-stage detection approaches. Building on these results, we further developed a comprehensive TNM classification system. Features extracted from the YOLO backbone were reduced using Principal Component Analysis (PCA) to enhance computational efficiency. These reduced features were then fed into a custom TNMClassifier, a neural network designed to classify the Tumor, Node, and Metastasis (TNM) stages. The TNMClassifier architecture comprises fully connected layers and dropout layers to prevent overfitting, achieving an accuracy of 98% in classifying the TNM stages. Additionally, we tested the YOLOv8 Small model on another dataset, the Lung3 dataset from the Cancer Imaging Archive (TCIA). This testing yielded a recall of 0.91, further validating the model’s effectiveness in accurately identifying lung cancer cases. The integrated system of YOLO for subtype detection and the TNMClassifier for stage classification shows significant potential to assist healthcare professionals in expediting and refining diagnoses, thereby contributing to improved patient health outcomes.

In this paper, we describe Remote Monitoring Validation Engineering System (ReMoVES), a newly-developed platform for motion rehabilitation through serious games and biophysical sensors. The main features of the system are highlighted as follows: motion tracking capabilities through Microsoft Kinect V2 and Leap Motion are disclosed and compared with other solutions; the emotional state of the patient is evaluated with heart rate measurements and electrodermal activity monitored by Microsoft Band 2 during the execution of the functional exercises planned by the therapist. The ReMoVES platform is conceived for home-based rehabilitation after the hospitalisation period, and the system will deploy machine learning techniques to provide an automated evaluation of the patient performance during the training. The algorithms should deliver effective reports to the therapist about the training performance while the patient exercises on their own. The game features that will be described in this manuscript represent the input for the training set, while the feedback provided by the therapist is the output. To face this supervised learning problem, we are describing the most significant features to be used as key indicators of the patient’s performance along with the evaluation of their accuracy in discriminating between good or bad patient actions.

Internet of Things (IoT) solutions are a concrete answer to many needs in the healthcare framework since they enable remote support for patients and foster continuity of care. Currently, frail elderly people are among end users who most need and would benefit from IoT solutions from both a social and a healthcare point of view. Indeed, IoT technologies can provide a set of services to monitor the healthcare of the elderly or support them in order to reduce the risk of injuries, and preserve their motor and cognitive abilities. The main feature of IoT solutions for the elderly population is ease of use. Indeed, to fully exploit the potential of an IoT solution, patients should be able to autonomously deal with it. The remote-monitoring validation engineering system (ReMoVES) described here is an IoT solution that caters to the specific needs of frail elderly individuals. Its architecture was designed for use at rehabilitation centers and at patients’ homes. The system is user-friendly and comfortably usable by persons who are not familiar with technology. In addition, exergames enhance patient engagement in order to curb therapy abandonment. Along with the technical presentation of the solution, a real-life scenario application is described referring to sit-to-stand activity.

This paper illustrates some applications of COSMO-SkyMed (CSK) observations for rapid mapping of flooded areas and damages in small to medium size catchments. The results presented here have been obtained within the framework of the project "OPERA Civil protection from floods" funded by the Italian Space Agency and run by a team of scientific research centres and private companies. The project aims to the systematic evaluation of the added value of the use of Earth Observation techniques into operational flood prediction chains. Due to the specific geomorphology of Italy, the focus is mainly on flash floods on small sized river catchments. Monitoring and modelling processes at proper space-time scales in this environment raise several issues to be solved, compared to applications in larger river basins. Here we address some related to the suitable use of CSK imagery.

Cultural heritage preservation is a crucial topic for our society. When dealing with fine art, color is a primary feature that encompasses much information related to the artwork’s conservation status and to the pigments’ composition. As an alternative to more sophisticated devices, the analysis and identification of color pigments may be addressed via a digital camera, i.e., a non-invasive, inexpensive, and portable tool for studying large surfaces. In the present study, we propose a new supervised approach to camera characterization based on clustered data in order to address the homoscedasticity of the acquired data. The experimental phase is conducted on a real pictorial dataset, where pigments are grouped according to their chromatic or chemical properties. The results show that such a procedure leads to better characterization with respect to state-of-the-art methods. In addition, the present study introduces a method to deal with organic pigments in a quantitative visual approach.