An Architecture based on CNNs and BiLSTMs for Slice-Level and Series-Level Intracranial Hemorrhage Identification in CT Scans - Comério, Daniel Henrique; Komati, Karin Satie; Oliveira-Santos, Thiago; Mutz, Filipe (Anais do Workshop de Visão Computacional (WVC); 2023: Anais do XVIII Workshop de Visão ComputacionalDO - 10.5753/wvc.2023.27525, 2023)
This work proposes an architecture of neural networks for estimating the probability of intracranial hemorrhage and its subtypes in computed tomography images and series. The architecture consists of three stages, with the first being a CNN and the other two being BiLSTM recurrent networks. The first stage receives as input a CT image and returns the hemorrhages probabilities. The second stage improves these estimates using contextual information from neighboring images. The final stage integrates the predictions from all slices in order to provide an unified output for the series. Extensive experiments were performed using the datasets RNSA, CQ500, and PhysioNet for evaluating configurations of the architecture, improvements produced by each component, and generalization to new data. The best configuration uses the DenseNet-121 as backbone and achieved average accuracy, precision, recall and f1-score over datasets of 91%, 91%, 90% and 90% confirming the model’s robustness and generalization.