Abstract
Cataract is one of the most common eye diseases, which occupies 4.2% of the population all over the world. Automatic cataract detection not only can help people prevent visual impairment and decrease the possibility of blindness but also can save the medical resources. Previous researchers have achieved automatic medical images detection using the Convolution Neural Network (CNN), which may be non-transparent, unexplained and doubtful. In this paper, we propose a novel idea of interpretable learning for explaining the result of cataract detection generated by CNNs, which is a result-oriented explanation. The AlexNet-CAM and GoogLeNet-CAM are reestablished on basis of AlexNet and GoogLeNet by replacing two fully-connected layers with global average pooling layer. Four models are used to test whether class activation map** (CAM) make the accuracy dropped. Then, we use gradient-class activation map** (Grad-CAM) combined with existed fine-grained visualization to generate heat-maps that show the important pathological features clearly. As a result, the accuracy of AlexNet (GoogLeNet) is 94.48% (94.89%), and that of AlexNet-CAM (GoogLeNet-CAM) is 93.28% (94.93%). Heat-maps corresponding with non-cataract fundus images highlighted the lens and parts of big vessels and small vessels; and the clarity of three kinds of heat-maps corresponding with cataract images declined in turn, which are mild, medium and severe. The results prove our approaches can keep the accuracy stable and increase the interpretability for cataract detection, which also can be generalized to any fundus image diagnosis in the medical field.
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Acknowledgements
This work is supported by the project of the State of Key Program of National Natural Science of China (Grant No. 71432004) and China National Science and Technology Major Project with no. 2017YFB1400803.
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Li, J. et al. (2019). Interpretable Learning: A Result-Oriented Explanation for Automatic Cataract Detection. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2018. Lecture Notes in Electrical Engineering, vol 542. Springer, Singapore. https://doi.org/10.1007/978-981-13-3648-5_33
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