Divisible Cell-Segmentation: A New Approach for Stroke Detection and Segmentation in CT Scans Using Deep Learning and Fine-tuning

Different pathologies can cause different public health problems and diseases that can cause the death of millions of people. Hemorrhagic stroke is a silent disease affecting millions of people worldwide, causing disorders and, in most cases, being fatal. CAD systems are systems capable of aiding medical diagnosis, and studies based on computed tomography images have been increasingly widespread in the literature and in intelligent applications aimed at medical diagnosis by images. The proposed study brings a new approach based on the use of deep learning for the detection of hemorrhagic stroke, in which it obtained an accuracy of 99.37% in the detection of stroke. A new stroke segmentation method called (Divisible Cell-Segmentation) was developed and used to delineate stroke borders by means of fine-tuning. The method is based on initialization within the hem-orrhagic stroke previously detected by the Detectron2 network using the concept of splittable cells for segmentation, where they multiply within the detected image, causing the segmentation of the edge of the hemorrhagic stroke when traveling through the stroke where they divide into the two-dimensional region. The New method obtained excellent results with 99.82% accuracy for the segmentation of CT images of cerebral hemorrhagic strokes, surpassing the state-of-the-art. The new method presented an optimization in processing time, surpassing works found in the literature, as well as greater efficiency and computational cost in the detection and segmentation of hemorrhagic stroke edges in computed tomography images.