An IoT platform for the analysis of brain CT images based on Parzen analysis

Stroke or cerebrovascular accidents are among the three leading causes of death worldwide. Furthermore, strokes are a major cause of morbidity, hospitalizations, and acquired disabilities. Stroke diagnoses are usually made based on the set of symptoms exhibited and, more specifically, on the results of neuroimaging exams. Among the different types of neuroimaging, computed tomography (CT) is the most commonly used because it can show the extent and severity of the accident. Furthermore, CT exams are faster, more accessible than other systems, and are financially viable. Thus, due to its emergency nature, CAD systems that can analyze CT images are essential to obtain information that can accelerate the diagnosis and definition of appropriate treatment. This work presents a new feature extractor from CT images of the brain to assist in the detection and classification of strokes, called Parzen Analysis of Brain Tissue Densities. The proposed method can reduce the subjectivity present in the brain tissue bands using Parzen Window Estimation by calculating the probability that each pixel belongs to a preconfigured range. In addition, the method is fully integrated with an Internet of Things framework and can be used remotely to assist medical specialists in the diagnosis and treatment of strokes. The method presented promising results, reaching the highest values of accuracy, 98.41%, F1-Score, 97.61%, negative predictive value, 98.80%, and positive predictive value, 95.45%, among all the methods evaluated here. The results demonstrated the effectiveness of the method in extracting relevant features from brain CTs to describe if there is a stroke as well as determining its type. (C) 2019 Elsevier B.V. All rights reserved.