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In the first step, a pre-trained model (YOLO) was used to detect allsuspicious nod-ules. The YOLO model was re-trained using 397 CT images todetect the entire nodule in CT images. To maximize the sensitivity of themodel, a confidence level (the probability threshold for object detection) of0.3 was set for nodule detection in the first phase (ensuring the entiresuspicious nodules are detected from the input CT images). The aim of thehierarchy model is to detect and classify the entire lung nodules (from CTimages) with a low false-negative rate. Given the outcome of the first step, weproposed a 3D CNN classifier to analyze and classify the suspicious nodulesdetected by the YOLO model to achieve a nodule detection framework with a verylow false-negative rate. This framework was evaluated using the LUNA 16dataset, which consists of 888 CT images containing the location of 1186nodules and 4 non-nodules in the lung. Results: A large number of falsepositives were detected due to the low confidence level used in the YOLO model.Utilizing the 3D classifier, the accuracy of nodule detection was remarkablyenhanced. The YOLO model detected 294 suspicious nodules (out of 321) whenusing a confidence level of 50%, wherein there were 107 false positives (187true positives). By reducing the confidence level to 30%, 459 suspiciousnodules were identified by the YOLO model, wherein 138 were false positives,and 321 were true positives. When the outcome of the YOLO model with aconfidence level of 30% was fed into the 3D CNN classifier, a nodule detectionaccuracy of 98.4% and AUC of 98.9% were achieved Conclusion: The proposedframework resulted in a few false-negative and false-positives pre-dictions innodule detection from CT images. The proposed approach would be helpful indetecting pulmonary nodules from CT images as a decision support tool.

A hierarchical approach for pulmonary nodules identification from CT images using YOLO v5s nodule detection and 3D neural network classifier