Liver Lesion Detect SSD: Liver Lesion Detection with Single Shot Detector Utilizing ResNet-50 and ResNet-34 Backbones - S. Komal Kour (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Liver Lesion detection is a critical task in medical imaging, essential for early diagnosis and treatment planning, requiring high accuracy and efficiency to ensure effective patient care. This paper presents a robust approach to liver lesion detection using a Single Shot Detector (SSD) framework with ResNet-50 and ResNet-34 as a backbone networks. The SSD model, known for its object detection capabilities, is enhanced by the deep feature extraction power of ResNet architectures, enabling precise and fast identification of liver lesions. We comprehensively evaluate the performance of the proposed method on the DeepLesion dataset from the National Institutes of Health, demonstrating significant improvements in detection accuracy compared to conventional approaches. The ResNet-34 backbone achieves an impressive Mean Average Precision (mAP) of 91%, with ResNet-50 achieving 85%, demonstrating their effectiveness in accurately localizing liver lesions. The ResNet-50 and ResNet-34 backbones provide a balanced trade-off between computational complexity and detection accuracy, making the model suitable for practical medical applications. This research showcases the potential of SSD with ResNet architectures for accurate and efficient liver lesion detection, offering promising advancements for clinical applications.