Abstract
Pressure ulcers are a common, painful, costly, and often preventable complication associated with prolonged immobility in bedridden patients. It is a significant health problem worldwide because it is frequently seen in inpatients and has high treatment costs. For the treatment to be effective and to ensure an international standardization for all patients, it is essential that the diagnosis of pressure ulcers is made in the early stages and correctly. Since invasive methods of obtaining information can be painful for patients, different methods are used to make a correct diagnosis. Image-based diagnosis method is one of them. By using images obtained from patients, it will be possible to obtain successful results by keeping patients away from such painful situations. At this stage, disposable wound rulers are used in clinical practice to measure the length, width, and depth of patients' wounds. The information obtained is then entered into tools such as the Braden Scale, the Norton Scale, and the Waterlow Scale to provide a formal assessment of risk for pressure ulcers. This paper presents a novel benchmark dataset containing pressure ulcer images and a semi-two-stream approach that uses the original images and the cropped wound areas together for diagnosing the stage of pressure ulcers. Various state-of-the-art convolutional neural network (CNN) architectures are evaluated on this dataset. Our experimental results (test accuracy of 93%, the precision of 93%, the recall of 92%, and the F1-score of 93%) show that the proposed semi-two-stream method improves recognition results compared to the base CNN architectures.