The abnormal traits and colors of feces typically indicate that the patients are probably suffering from tumor or digestive-system diseases. Thus a fast, accurate and automatic health diagnosis system based on feces is urgently necessary for improving the examination speed and reducing the infection risk. The rarity of the pathological images would deteriorate the accuracy performance of the trained models. In order to alleviate this problem, we employ augmentation and over-sampling to expand the samples of the classes that have few samples in the training batch. In order to achieve an impressive recognition performance and leverage the latent correlation between the traits and colors of feces pathological samples, a multi-task network is developed to recognize colors and traits of the macroscopic feces images. The parameter number of a single multi-task network is generally much smaller than the total parameter number of multiple single-task networks, so the storage cost is reduced. The loss function of the multi-task network is the weighted sum of the losses of the two tasks. In this paper, the weights of the tasks are determined according to their difficulty levels that are measured by the fitted linear functions. The sufficient experiments confirm that the proposed method can yield higher accuracies, and the efficiency is also improved.
Keywords: Color recognition; Data augmentation, over-sampling; Macroscopic feces image; Multi-task diagnosing; Trait recognition; Weighting selection.
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