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• Zinovev, D
• Raicu, D
• Furst, J
• Armato III, SG.
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• Zinovev, D
• Raicu, D
• Furst, J
• Armato III, SG.
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• Raicu, D
• Furst, J
• Armato III, SG.

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Algorithms 2009, 2(4), 1473-1502; doi:10.3390/a2041473

Article

Predicting Radiological Panel Opinions Using a Panel of Machine Learning Classifiers

Dmitriy Zinovev ^1,* , Daniela Raicu ¹ , Jacob Furst ¹  and Samuel G. Armato III ²

¹ Intelligent Multimedia Processing Laboratory, College of Computing and Digital Media, DePaul University, Chicago, IL 60604, USA ² Department of Radiology, The University of Chicago, Chicago, IL 60637, USA

* Author to whom correspondence should be addressed.

Received: 27 September 2009; in revised form: 27 October 2009 / Accepted: 11 November 2009 / Published: 30 November 2009

(This article belongs to the Special Issue Machine Learning for Medical Imaging)

Download PDF Full-Text [650 KB, uploaded 30 November 2009 14:07 CET]

Abstract: This paper uses an ensemble of classifiers and active learning strategies to predict radiologists’ assessment of the nodules of the Lung Image Database Consortium (LIDC). In particular, the paper presents machine learning classifiers that model agreement among ratings in seven semantic characteristics: spiculation, lobulation, texture, sphericity, margin, subtlety, and malignancy. The ensemble of classifiers (which can be considered as a computer panel of experts) uses 64 image features of the nodules across four categories (shape, intensity, texture, and size) to predict semantic characteristics. The active learning begins the training phase with nodules on which radiologists’ semantic ratings agree, and incrementally learns how to classify nodules on which the radiologists do not agree. Using our proposed approach, the classification accuracy of the ensemble of classifiers is higher than the accuracy of a single classifier. In the long run, our proposed approach can be used to increase consistency among radiological interpretations by providing physicians a “second read”.

Keywords: ensemble learning; LIDC; lung nodule classification

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