01776nas a2200313   4500000000100000008004100001260001200042653002200054653001200076653002300088653001900111653001800130100001300148700001400161700001500175700001800190700001200208700001300220700001500233700001500248700001300263700001500276700001500291700002000306245009500326490000800421520101900429022001401448       2023                            d  c08/202310aHealth strategies10aleprosy10aNursing Assessment10aTelemonitoring10aWound Healing1 aMonroy B1 aSanchez K1 aArguello P1 aEstupiñán J1 aBacca J1 aCorrea C1 aValencia L1 aCastillo J1 aMieles O1 aArguello H1 aCastillo S1 aRojas-Morales F00aAutomated chronic wounds medical assessment and tracking framework based on deep learning.0 v1653 a<p>Chronic wounds are a latent health problem worldwide, due to high incidence of diseases such as diabetes and Hansen. Typically, wound evolution is tracked by medical staff through visual inspection, which becomes problematic for patients in rural areas with poor transportation and medical infrastructure. Alternatively, the design of software platforms for medical imaging applications has been increasingly prioritized. This work presents a framework for chronic wound tracking based on deep learning, which works on RGB images captured with smartphones, avoiding bulky and complicated acquisition setups. The framework integrates mainstream algorithms for medical image processing, including wound detection, segmentation, as well as quantitative analysis of area and perimeter. Additionally, a new chronic wounds dataset from leprosy patients is provided to the scientific community. Conducted experiments demonstrate the validity and accuracy of the proposed framework, with up to 84.5% in precision.</p>
  a1879-0534