Abstract
Distinguishing between different sub-classes of Acute Lymphoblastic leukemia (ALL) based on morphological differences in blood smear images is challenging. Deep learning methods have been successful for morphological classification. This paper aims to develop a deep transfer fusion model (TFDNet) to predict ALL sub-classes using a few blood cell images. TFDNet is a customized Convolutional Neural Network (CNN) that consists of two transfer learning modules, Xception and Dense, working in parallel for feature extraction. TFDNet then utilizes a two-branch feature extraction layer to fuse the multi-scale features for the diagnosis of ALL sub-classes. To evaluate the effectiveness and generalizability of TFDNet, we compare it with seven state-of-the-art methods on five different datasets, including three small-sample ALL types, as well as skin cancer and brain cancer data. The experimental results demonstrate that TFDNet outperforms the seven state-of-the-art methods on all five datasets.
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Availability and Implementation
All the code and image data in this paper are available at https://github.com/xin242328/TFDNet, which allows researchers to replicate our experiments to verify the results and use the methods and data for further research in their own fields. And All models were implemented using tensorflow-gpu (version 2.4.0), and all training processes were trained on the GPU (GeForce GTX 1080) in Linux.
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Acknowledgment
This work was supported by the National Key R&D Program of China under Grant 2020YFA0908700, the National Nature Science Foundation of China under Grant 62176164, 62203134, the Natural Science Foundation of Guangdong Province under Grant 2023A1515010992, Science and Technology Innovation Committee Foundation of Shenzhen City under Grant JCYJ20220531101217039.
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Du, Z., Xia, X., Fang, M., Yu, L., Li, J. (2023). A Deep Transfer Fusion Model for Recognition of Acute Lymphoblastic Leukemia with Few Samples. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_59
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