International Conference on Computer and Knowledge Engineering

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Intracranial Hemorrhage Classification using CBAM Attention Module and Convolutional Neural Networks

Authors :

Parnian Rahimi¹ Marjan Naderan² Amir Jamshidnezhad³ Shahram Rafie⁴

1- Shahid Chamran University of Ahvaz 2- Shahid Chamran University of Ahvaz 3- Ahvaz Jundishapur University of Medical Sciences 4- Ahvaz Jundishapur University of Medical Sciences

Keywords :

Intracranial Hemorrhage،Convolutional Neural Networks،Attention Mechanism،Focal Loss

Abstract :

Intracerebral hemorrhage is a life-threatening condition characterized by bleeding within the brain. The conventional methods for detecting and diagnosing ICH are often time-consuming and complex. However, recent advancements in deep learning algorithms have shown promising results in improving the accuracy and efficiency of ICH detection and classification. In this paper, we present the CBAM-NeXt model, a fusion of the Convolutional Block Attention Module (CBAM) and the ResNeXt-101 architecture. The CBAM module incorporates both Channel attention and spatial attention mechanisms, allowing the model to selectively focus on more informative parts of input images. Furthermore, we employ the Extreme Gradient Boosting (XGBoost) algorithm to classify the extracted features. Our proposed method was trained and evaluated on the RSNA Intracranial Hemorrhage CT challenge (2019) dataset. The results illustrate that our proposed method obtained the average F1 score and average accuracy of 70% and 97.9%, respectively. Our results outperform state-of-the-art baselines in the RSNA challenge and benchmark.