An End-to-End Process for Cancer Identification from Images of Lung Tissue

This research describes a non-interactive process that applies several forms of computational intelligence to the task of classifying biopsy lung tissue samples based on visual data in the form of raw digital photographs of those samples. The three types of lung cancer evaluated (squamous cell carcinoma, adenocarcinoma, and bronchioalveolar carcinoma) together account for 65-70% of lung cancer diagnoses. The accuracy of the process on the test data supports the hypothesis that an accurate predictive model can be generated from the training images. The fact that the performance of the process on the independent test data set is comparable to the one-hold-out performance on the training data alone also supports the hypothesis that the performance achieved in this study is an accurate baseline for the processes potential performance against much larger quantities of data.

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