Multistage transfer learning technique for classifying rare medical datasets

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It is said that about 8% of the people across the world are impacted by different kinds of rare diseases. Identifying such rare diseases accurately is a challenging task, as they exhibit common symptoms that may be incorrectly recognized as a common disease. As a result, the treatment is insufficient and by the time a diagnosis is made, it may be too late for the patient to survive. Therefore, any attempt towards early diagnosis of rare diseases becomes the need of the hour and several researchers are concentrating on machine learning techniques to do the same. This work proposes one such approach to diagnose rare disorders. A multi-level transfer learning (MLTL) framework with three models is designed to detect and classify rare diseases with very limited datasets, using the knowledge acquired from easily available datasets. The first model, for the source domain, uses the abundantly available non-medical images and learns the generalized features. The acquired knowledge is then transferred to the second model, for the intermediate and auxiliary domain, which is again commonly available and related to the target domain, and helps in learning the auxiliary or intermediate task. This information is then used to classify the final target domain, which consists of medical datasets that are very scarce. Experimental results with non-medical and the auxiliary CT abdomen images show good classification accuracies for identifying rare diseases related to the liver and kidneys. An area under receiver operating characteristic (ROC) curve of 0.90 and 0.89 are achieved for two different rare diseases, with just 2.08% of the source domain dataset, 6.6% of the intermediate domain dataset and less than 10% of the rare target domain dataset, when compared to the work reported in the literature.