Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

Vidhya, V; Raghavendra, U.; Gudigar, Anjan; Kasula, Praneet; Chakole, Yashas; Hegde, Ajay; Menon, Girish; Ooi, Chui Ping; Ciaccio, Edward J.; Acharya, U. Rajendra

doi:10.3390/informatics9010004

Cited by 3 publications

(1 citation statement)

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“…Hence the CBAM module is also included along with the ECA module for strengthening the feature aggregation capability, which resulted in an improved mAP of 94.3%, thereby enhancing the performance of the YOLOV5s with SPPF network to 0.3%. Experiments (8)(9)(10)(11) were carried out to further improve the model with the introduction of the microscale head [47] and the Double ECA attention module [63]. However, it can be concluded that the YOLOV5s model with the combination of SPPF, ECA, and CBAM can perform optimized hematoma detection as compared to experiments ( 9) and ( 11) with an increase in mAP of 0.2% and 0.3% respectively.…”

Section: B Ablation Studymentioning

confidence: 95%

YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images

Vidhya,

Raghavendra,

Gudigar

et al. 2023

IEEE Access

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Intracranial hematoma due to traumatic brain injury is a serious health concern with rates of morbidity and mortality that are increasing worldwide. Manual identification is slow, subject to observer variabilities, and the existing automated techniques for intracranial hematoma detection in non-contrast computed tomography images cannot effectively detect multiple lesions of irregular sizes and shapes. Therefore, a computer-aided system using different window settings, YOLOv5s, cascaded attention module, and spatial pyramid pooling-fast is proposed to detect hematoma types, namely acute intraparenchymal, intraventricular, subdural, epidural, subarachnoid, and chronic subdural. Firstly, the computed tomography images are pre-processed using a window-based stacking approach wherein a three-channel image is generated by stacking grayscale images obtained with the aid of multiple window settings, i.e, brain, bone, and subdural. Secondly, a cascaded attention module is constructed in the neck of the YOLOv5s model to improve its detection performance by placing the convolution block attention module in serial with the efficient channel attention module. The cascaded attention module enriches the feature representation of various hematoma types in complex backgrounds especially when they are small and inconspicuous. The spatial pyramid pooling is replaced by a spatial pyramid pooling-fast to reduce the computational parameters and accelerate the feature fusion ability. The proposed deep learning model is trained, validated, and tested with 15,921 images from the brain haemorrhage extended dataset and it achieved overall precision, recall, F1score, and mean average precision at 0.5 and 0.5:0.95 of 0.935, 0.908, 0.921, 0.943 and 0.65 respectively. The experimental results show that in comparison to the original YOLOv5s model and state-of-the-art methods, the model was able to localize and classify the acute or chronic instances of five hematoma subtypes in an individual image with improved precision and recall values. Hence the proposed system can be used in hospitals for the early and accurate detection of hematoma.INDEX TERMS Cascaded attention module, medical image processing, intracranial hematoma, TBI, YOLO.

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