TL-LFF Net: transfer learning based lighter, faster, and frozen network for the detection of multi-scale mixed intracranial hemorrhages through genetic optimization algorithm

Abstract

Computed tomography (CT) is the most commonly used imaging method in intracranial hemorrhage (ICH). Although deep learning (DL) models are well suited for detecting and segmenting multi-class hemorrhages, localizing multi-scale mixed hemorrhages with limited resources such as bounding boxes is difficult. To address this issue, the current study proposes a novel transfer learning-based TL-LFF Network. To detect multi-scale mixed hemorrhages, the proposed model employs a backbone module that extracts in-depth features from the input images, and a spatial pyramid pooling faster layer that performs the pooling operation at various levels. In the neck section, a path aggregated network (PANet) is used to store spatial information. Furthermore, to achieve a lightweight nature, the proposed backbone and neck modules were frozen during the backpropagation stage, resulting in a decrease in detection accuracy. To improve detection capability while remaining lightweight, a concept known as transfer learning is used. This strategy significantly improves the accuracy of the proposed model. In addition, the Genetic Algorithm (GA) concept is used to optimize the hyperparameters, where the mutation is used to develop new offspring based on previous generations. The brain hemorrhage extended dataset was used to train and validate the proposed model. In terms of detection metrics and lightweight criteria, the experimental results showed that the proposed model performed better when compared to other existing models. As a result, we can use the proposed model in the clinical implementation stage to reduce the radiologist's CT scan read time.