Abstract
This paper presents experimental results obtained from using weakly tuned deep learning models for skeletal age estimation from hand radiographs. By leveraging transfer learning, deep learning models were initialised with the ImageNet dataset weights and then tuned for 5 epochs on the target RSNA Bone Age dataset. Thereafter, the deep learned models were used to estimate skeletal ages and obtained a MAE of 9.96 months. With the exploration of weighted model ensemble the MAE reduced to 9.32 months. In the final ensemble, the best results achieved took into consideration predictions from one of the poor performing models. This improvement indicates that weakly tuned deep learned models still have a good prediction accuracy. Moreover, poor performing models such as the InceptionResNetV2 which achieved a MAE of 16.75 months was still needed in the ensemble for best results. This indicates that even poor performing models can still extract useful information which can be taken advantage of in ensemble situations such as weighted ensemble.
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Hirasen, D., Pillay, V., Viriri, S., Gwetu, M. (2020). Skeletal Age Estimation from Hand Radiographs Using Transfer Learning. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_16
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DOI: https://doi.org/10.1007/978-3-030-66187-8_16
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