MRI Liver Image Assisted Diagnosis Based on Improved Faster R-CNN

Abstract

In response to challenges in liver occupancy such a variety of types and manifestations and difficulties in differentiating benign and malignant ones, this paper takes liver images of enhanced MRI scan as the research object, targets on the detection and identification of liver occupancy lesion areas and determining if it is benign or malignant. Accordingly, the paper proposes an auxiliary diagnosis method for liver image combining deep learning and MRI medical imaging. The first step is to establish a reusable standard dataset for MRI liver occupancy detection by pre-processing, image denoising, lesion annotation and data augmentation. Then it improves the classical region-based convolutional neural network (R-CNN) algorithm Faster R-CNN by incorporating CondenseNet feature extraction network, custom-designed anchor size and transfer learning pre-training. This is to further improve the detection accuracy and benign and malignant classification performance of liver occupancy. Experiments show that the improved model algorithm can effectively identify and localise liver occupancies in MRI images, and achieves a mean average precision (mAP) of 0.848 and an Area Under the Curve (AUC) of 0.926 on the MRI standard dataset. This study has important research significance and application value for reducing manual misses and misdiagnosis and improving the early clinical diagnosis rate of liver cancer.

Enhanced Tool Detection in Industry 4.0 via Deep Learning-Augmented Human Intent Recognition: Introducing the Industry-RetinaNet Model

“…Amidst the swift advancements in deep learning, various object detection models such as RCNN, Fast RCNN, Faster RCNN, SSD, YOLO, and RetinaNet have been presented in the literature [16][17][18][19][20][21]. However, the unique challenges presented by tool detection, owing to the diversity in tool sizes, shapes, and appearances amidst intricate backgrounds, necessitate the formulation of a specialized detection mechanism (Figure 1).…”