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. 2023 Feb 9;13(4):652.
doi: 10.3390/diagnostics13040652.

Intracranial Hemorrhage Detection Using Parallel Deep Convolutional Models and Boosting Mechanism

Muhammad Asif  1 , Munam Ali Shah  1 , Hasan Ali Khattak  2 , Shafaq Mussadiq  3 , Ejaz Ahmed  4 , Emad Abouel Nasr  5 , Hafiz Tayyab Rauf  6
Affiliations

Intracranial Hemorrhage Detection Using Parallel Deep Convolutional Models and Boosting Mechanism

Muhammad Asif et al. Diagnostics (Basel). .

Abstract

Intracranial hemorrhage (ICH) can lead to death or disability, which requires immediate action from radiologists. Due to the heavy workload, less experienced staff, and the complexity of subtle hemorrhages, a more intelligent and automated system is necessary to detect ICH. In literature, many artificial-intelligence-based methods are proposed. However, they are less accurate for ICH detection and subtype classification. Therefore, in this paper, we present a new methodology to improve the detection and subtype classification of ICH based on two parallel paths and a boosting technique. The first path employs the architecture of ResNet101-V2 to extract potential features from windowed slices, whereas Inception-V4 captures significant spatial information in the second path. Afterwards, the detection and subtype classification of ICH is performed by the light gradient boosting machine (LGBM) using the outputs of ResNet101-V2 and Inception-V4. Thus, the combined solution, known as ResNet101-V2, Inception-V4, and LGBM (Res-Inc-LGBM), is trained and tested over the brain computed tomography (CT) scans of CQ500 and Radiological Society of North America (RSNA) datasets. The experimental results state that the proposed solution efficiently obtains 97.7% accuracy, 96.5% sensitivity, and 97.4% F1 score using the RSNA dataset. Moreover, the proposed Res-Inc-LGBM outperforms the standard benchmarks for the detection and subtype classification of ICH regarding the accuracy, sensitivity, and F1 score. The results prove the significance of the proposed solution for its real-time application.

Keywords: computed tomography; convolutional neural networks; intracranial hemorrhage; light gradient boosting machine; support vector machine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Systematic view of the proposed methodology for detection and subtype classification of ICH.
Figure 2
Figure 2
Overview of subtypes of ICH.
Figure 3
Figure 3
Representation of subtypes of ICH.
Figure 4
Figure 4
Representation of three intensity windows.
Figure 5
Figure 5
Loss analysis of Inception-V4 during training.
Figure 6
Figure 6
Accuracy of Inception-V4 during training.
Figure 7
Figure 7
AUC during the training Inception-V4.
Figure 8
Figure 8
AUPR of Inception-V4 during execution.
Figure 9
Figure 9
Loss analysis of ResNet101-V2 during training.
Figure 10
Figure 10
Accuracy of ResNet101-V2 during training.
Figure 11
Figure 11
AUC during the training ResNet101-V2.
Figure 12
Figure 12
AUPR of ResNet101-V2 during execution.
Figure 13
Figure 13
AUC-based performance of the proposed solution.
Figure 14
Figure 14
Proposed solution’s performance based on AUPR.
See this image and copyright information in PMC

References

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