Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images
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Lung cancer is the leading cause of cancer death and morbidity worldwide with early detection being the key to a positive patient prognosis. Although a multitude of studies have demonstrated that machine learning, and particularly deep learning, techniques are effective at automatically diagnosing lung cancer, these techniques have yet to be clinically approved and accepted/adopted by the medical community. Rather than attempting to provide an artificial 'second reading' we instead focus on the automatic creation of viable decision tree models from publicly available data using computer vision and machine learning techniques. For a small inferencing dataset, this method achieves a best accuracy over 84 the malignant class. Furthermore, the decision trees created by this process may be considered as a starting point for refinement by medical experts into clinically usable multi-variate lung cancer scoring and diagnostic models.
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