Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning

Lung malignancy is one of the most common causes of death in the world caused by malignant lung nodules which commonly diagnosed radiologically by radiologists. Unfortunately, the continuous flow of medical images in hospitals drives radiologists to prioritize quantity over quality. This work condition allows misinterpretation especially on ambiguous anatomical structures that resemble lung nodule for example enlarged lymph nodes and resulting in decreasing sensitivity and accuracy of malignant lung nodule detections and late diagnosis proven to be fatal to patients. To address the problem, this paper proposed a novel lung nodule detection and classification model using one stage detector called as “I3DR-Net.” The model was formed by combining pre-trained natural images weight of Inflated 3D ConvNet (I3D) backbone with feature pyramid network to multi-scale 3D Thorax Computed tomography scan (CT-scan) dataset. I3DR-Net able to produce remarkable results on lung nodule texture detection task with mAP 49.61% and 22.86%, and area under curve (AUC) 81.84% and 70.36% for public and private dataset. Additionally, I3DR-Net successfully outperform previous state-of-the-art Retina U-Net and U-FRCNN + mean average precision (mAP) by 7.9% and 7.2% (57.71% VS 49.8% VS 50.5%) for malignant nodule detection and classification task.

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Results from the Paper

 Ranked #1 on Lung Nodule 3D Detection on LIDC-IDRI (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Lung Nodule 3D Detection LIDC-IDRI I3DR-Net mean average precision 49.61 # 1
AUC 81.84 # 1
Sensitivity 94.12 # 1
Lung Nodule Classification LIDC-IDRI I3DR-Net AUC 81.84 # 5
Lung Nodule Detection LIDC-IDRI I3DR-Net AUC 81.84 # 1
mean average precision 49.61 # 1
Sensitivity 94.12 # 1
Lung Nodule 3D Classification LIDC-IDRI I3DR-Net AUC 81.84 # 1