Abstract
Early detection of lung cancer increases a patient’s survival rate and provides healthcare professionals, valuable time, and information to administer effective treatment. Lung nodules are early signs of lung cancer. Computer-aided diagnostic systems that can identify pulmonary nodules improve early detection as well as provide an independent second opinion. We propose an automated workflow for follow-up recommendation based on low-dose computed tomography (LDCT) images using deep learning, as per 2017 Fleischner Society guidelines. As per guidelines, follow-up is based on size, volume and texture of nodules. In this paper, we present a 5 stage approach for automated follow-up recommendation. The 5 stages are Lung segmentation, Nodule detection and False Positive Reduction (FPR), Texture classification, Nodule segmentation and Follow-up recommendation. Our nodule detection has a sensitivity of 94% @ 1 false positive per scan. The FPR network improves the specificity of detection to 90% without changing sensitivity. Nodule segmentation has a Jaccard index of 0.77 on 768 nodules from Lung Nodule Database (LNDb) [1]. Texture classification has a sensitivity of 97% on solid nodules and a Fleiss-Cohen’s Kappa of 0.37 on LNDb data with most errors between sub-solid and solid nodules. Our rule-based follow-up recommendation has a Fleiss-Cohen’s Kappa of 0.53 on 236 patients from LNDb. In conclusion, we found that rule-based approach for follow-up alongside deep learning models is the best approach in achieving best results. As we improve the first 4 stages, we foresee that recommendation from AI will become closer to radiologists recommendation.
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Kaluva, K.C., Vaidhya, K., Chunduru, A., Tarai, S., Nadimpalli, S.P.P., Vaidya, S. (2020). An Automated Workflow for Lung Nodule Follow-Up Recommendation Using Deep Learning. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_32
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DOI: https://doi.org/10.1007/978-3-030-50516-5_32
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