Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework

Alexander Sóñora-Mengan; Panagiotis Gonidakis; Bart Jansen; Juan Garcı́a-Naranjo; Jef Vandemeulebroucke

doi:10.1117/12.2549778

16 March 2020 Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework

Alexander Sóñora-Mengan, Panagiotis Gonidakis, Bart Jansen, Juan Garcı́a-Naranjo, Jef Vandemeulebroucke

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113143T (2020) https://doi.org/10.1117/12.2549778
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Computer aided diagnosis systems are used to assist radiologists in their decision making. The sensitivity of these systems is hindered by the complexity of the structures inside the lungs. Several systems and methods have been proposed to detect and classify lung nodules, but all of them have their strengths and weaknesses. One way to overcome the weaknesses is to combine multiple systems. Systems based on handcrafted features capture a limited set of characteristics from the image, while deep learning based classifiers can deal with a wider range of structures. In this work, several ways to combine a handcrafted feature based classifier with four convolutional neural network are explored. The systems were combined merging the probabilities assigned to the detections in several ways. Support-vector machine, multilayer perceptron and random forest classifiers were used to combine the selected classifiers. The LUNA16 Challenge was used to evaluate the performance of the resulting hybrid systems. In all cases, the hybrid systems outperformed the individual systems. Although the average of sensitivities are similar for most of the combinations, the best hybrid system achieves a gain of 35 extra nodules at 4 FP per scan.

Citation Download Citation

Alexander Sóñora-Mengan, Panagiotis Gonidakis, Bart Jansen, Juan Garcı́a-Naranjo, and Jef Vandemeulebroucke "Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143T (16 March 2020); https://doi.org/10.1117/12.2549778

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available