Faster R-CNN vs SSD: A Deep Learning Approach for Nodule Detection in Lung Cancer Diagnosis

Deep learning-based object identification technology has numerous uses, including facial recognition, commercial analytics, and medical imaging analysis. An object detector has a backbone for extracting features and a head for classifying items and predicting bounding boxes. In this work, Single Shot Detector and Faster R-CNN are used as detectors for lung cancer diagnosis. Both these detectors were trained for nodule identification, and their outcomes were compared. The findings indicate that SSD attains an accuracy of 43.80% whereas Faster R-CNN achieves the accuracy of 95.80%.