Enhancing sensitivity in lung nodule malignancy classification: incorporating cost values into deep learning-based CAD systems

Yiyang Wang; Charmi Patel; Thiruvarangan Ramaraj; Roselyne Tchoua; Jacob Furst; Daniela Raicu

doi:10.1117/12.3006031

3 April 2024 Enhancing sensitivity in lung nodule malignancy classification: incorporating cost values into deep learning-based CAD systems

Yiyang Wang, Charmi Patel, Thiruvarangan Ramaraj, Roselyne Tchoua, Jacob Furst, Daniela Raicu

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129272M (2024) https://doi.org/10.1117/12.3006031
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Traditional deep learning models have been extensively utilized in lung cancer computer-aided diagnosis (CAD) studies. These models typically treat false positive and false negative cases equally during training. However, in the specific context of lung nodule malignancy CAD studies, our objective is to improve sensitivity without significantly compromising overall accuracy. To address this, our study proposes the incorporation of cost values into the sigmoid activation function for deep learning-based CAD systems used in lung nodule malignancy classification. Through empirical analysis, we observed a significant 4% increase in sensitivity while effectively maintaining the overall accuracy. The results obtained from our study provide compelling evidence that incorporating cost values into the training scheme can significantly enhance sensitivity in the classification of lung nodule malignancy. Furthermore, we emphasize the importance of considering the cost values as hyperparameters in future CAD systems. By appropriately tuning these cost values, we can further optimize the performance and efficacy of lung nodule malignancy CAD systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yiyang Wang, Charmi Patel, Thiruvarangan Ramaraj, Roselyne Tchoua, Jacob Furst, and Daniela Raicu "Enhancing sensitivity in lung nodule malignancy classification: incorporating cost values into deep learning-based CAD systems", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129272M (3 April 2024); https://doi.org/10.1117/12.3006031

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