A5032439083- Diabetic Foot Ulcer Assessment and Management
- Pressure Ulcer Prevention and Management
- Cervical Cancer and HPV Research
- Digital Imaging for Blood Diseases
- AI in cancer detection
- Wound Healing and Treatments
- COVID-19 diagnosis using AI
- Advanced Neural Network Applications
Fraunhofer Portugal Research
2021-2023
ORCID
2021
Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, visual examination microscopic slides is challenging, time-consuming, and ambiguous task. Shortages specialized staff equipment are increasing interest developing artificial intelligence (AI)-powered portable solutions support screening programs. This paper presents novel approach based on RetinaNet model with ResNet50...
Given the current prevalence and impact of cervical cancer worldwide, many technological developments focused on automating screening process have arisen recently. Nonetheless, there is still a clear need for affordable, portable automated IoT-based solutions to expand coverage programs worldwide. This particularly relevant lower-resource countries, which account 88% all cancer-related deaths. work proposes low-cost, smartphone-based microscopy device analysis liquid-based cytology samples,...
Wound monitoring is a time-consuming and error-prone activity performed daily by healthcare professionals.Capturing wound images crucial in the current clinical practice, though image inadequacy can undermine further assessments.To provide sufficient information for analysis, should also contain minimal periwound area.This work proposes an automatic acquisition methodology that exploits deep learning models to guarantee compliance with mentioned adequacy requirements, using marker as metric...
Wound dressings and their proper management are crucial to the wound's recovery. This process can be time-consuming requires special knowledge effective. In order improve monitorization decision-making processes, this work proposes a framework based on state-of-the-art Deep Learning models for automating acquisition analysis of wound dressings. Its development was supported by novel dataset dressing images annotated experts regarding state regions interest. The two-step pipeline resorts...