Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis

Strokes are one of the leading causes of death in the world. Despite the high mortality rate, chances of recovery are high when an accurate diagnosis is made quickly, and appropriate treatment is provided. Several types of neuroimaging techniques are used to detect strokes, and computed tomography (CT) and magnetic resonance imaging are the main ones. Although magnetic resonance imaging shows clearer results, CT is, in most cases, the most viable alternative, due to the reduced examination time and low cost. Several computeraided diagnostic systems have been developed in recent years with a focus on the Internet of Things (IoT). These systems, which establish rapid communication between the IoT devices, provide greater integration between specialists and patients, and consequently, a better medical follow up. However, stroke detection and classification techniques in IoT devices require that these methods developed methods have low computational and low storage costs. Thus, Edge computing devices have been attracting attention for their excellent processing capabilities, providing a layer between the IoT device and the cloud. This work proposes a new feature extractor for brain CT images based on an Adaptive Analysis of Brain Tissue Densities. The proposed method presented promising results of accuracy and F1-score, reaching 98.13% and 97.83%, respectively, surpassing several state-of-the-art methods. Furthermore, the proposed method presents low computational cost, with an average extraction time of 0.087s per image, and is, thus, a viable option for integration in IoT and Edge Computing devices, by providing rapid detection and classification of strokes.