DRAT: A semi-supervised tool for automatic annotation of lesions caused by diabetic retinopathy
Resumo
Context: Diabetes is a significant global public health concern, with a growing number of affected. Patients with diabetes experience a reduced quality of life, primarily due to complications such as diabetic retinopathy. This complication, affecting a substantial portion of individuals with diabetes, is one of the leading causes of vision loss in adults. However, vision loss can be prevented through early diagnosis. Problem: Developing computational models for diagnosis is challenging due to the lack of datasets with adequate annotations, which are expensive and time-consuming to create. Solution: We introduce the Diabetic Retinopathy Annotation Tool, enabling automated annotation of retinal lesions in fundus images, expediting the process and allowing expert corrections. IS theory: This article incorporates ideas from Soft Systems Theory. Method: This research can be classified as explanatory, as it aims to establish a comprehensive theory by analyzing the results of experiments. This article employed a case study methodology to thoroughly examine fundus lesions, aiding in the creation of a tool for annotating and identifying these lesions. After, conducted experimental analysis to quantitatively evaluate the deep neural network model’s ability to predict and automatically label retinal lesions. Results: The model achieved an mAP of 0.4390 on the validation dataset and 0.3002 on the test dataset from the DDR dataset. Additionally, the tool demonstrated promising results when applied to the IDRID dataset, compared to actual lesions. Contributions and Impact in the IS area: This work introduces a tool for the healthcare field, potentially aiding in diagnosing diabetic retinopathy. The study also presents image processing techniques and computational model training methods applicable to future healthcare-oriented research.
