Abstract
Acute Lymphoblastic Leukemia (ALL) is a disease that is caused by the uncontrollable growth of immature and malignant White Blood Cells (WBCs) which are called lymphoblasts. It occurs when the bone marrow contains 20% or more lymphoblasts. Therefore, Leukemia is diagnosed by counting White Blood Cells (WBCs) in the microscopic smears of bone marrow and blood. There are several attempts for effective leukemia classification with the help of computer aided-based methods by analyzing microscopic smear images. However, how to further improve the classification accuracy is still challenging. Therefore, in this paper, we propose an end-to-end framework to classify ALL. First, we train a more robust specific pre-trained Vision Transformer (ViT) trained on the same type of data, ALL-IDB1. Then, we examine the extracted features of Deep ViT across layers and utilize these features as dense descriptors to make predictions. Based on the observation in this paper, we find and empirically demonstrate that such features, when extracted from the ViT model, consistently improve the classification performance. Moreover, we observe that the pre-trained ViT which is fine-tuned on specific dataset is more effective than the general images dataset. We show that our proposed method achieves an average accuracy of 98.75% which is a competitive result with recent state-of-the-art by a large margin.
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References
ALL-IDB Homepage.https://homes.di.unimi.it/scotti/all/. Accessed 25 Oct 2023
Rastogi, P., Khanna, K., Singh, V.: LeuFeatx: deep learning-based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear. Comput. Biol. Med. 142, 105236 (2022)
Das, P., Meher, S.: An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia. Expert Syst. Appl. 183, 115311 (2021)
Sahlol, A., Kollmannsberger, P., Ewees, A.: Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Sci. Rep. 10(1), 2536 (2020)
Cho, P., Dash, S., Tsaris, A., Yoon, H.J.: Image transformers for classifying acute lymphoblastic leukemia. In: Medical Imaging 2022: Computer-Aided Diagnosis, vol. 12033, pp. 647–653. SPIE (2022)
Khandekar, R., Shastry, P., Jaishankar, S., Faust, O., Sampathila, N.: Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis. Biomed. Signal Process. Control 68, 102690 (2021)
Gokulkrishnan, N., Nayak, T., Sampathila, N.: Deep learning-based analysis of blood smear images for detection of acute lymphoblastic leukemia. In 2023 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1–5. IEEE (2023)
Claro, M., et al.: Assessing the impact of data augmentation and a combination of CNNs on leukemia classification. Inf. Sci. 609, 1010–1029 (2022)
Jawahar, M., Sharen, H., Jani, L., Gandomi, A.: ALNett: a cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification. Comput. Biol. Med. 148, 105894 (2022)
Zeng, W., et al.: Microscopic hyperspectral image classification based on fusion transformer with parallel CNN. IEEE J. Biomed. Health Inform. (2023)
Aitazaz, T., Tubaishat, A., Al-Obeidat, F., Shah, B., Zia, T., Tariq, T.: Transfer learning for histopathology images: an empirical study. Neural Comput. Appl. 35(11), 7963–7974 (2023)
Selvano, E., Paulindino, A., Elwirehardja, G., Pardamean, B.: Evaluating self-supervised pre-trained vision transformer on imbalanced data for lung disease classification. ICIC Express Lett. Part B: Appl. (2023)
Asiri, A., et al.: Advancing brain tumor classification through fine-tuned vision transformers: a comparative study of pre-trained models. Sensors 23(18), 7913 (2023)
Yang, G., Luo, S., Greer, P.: A novel vision transformer model for skin cancer classification. Neural Process. Lett. 55(7), 9335–9351 (2023)
Shanmugam, J., Duraisamy, B., Simon, B., Bhaskaran, P.: Alzheimer’s disease classification using pre-trained deep networks. Biomed. Signal Process. Control 71, 103217 (2022)
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv: pp2010.11929 (2020)
Naseer, M., Ranasinghe, K., Khan, S., Hayat, M., Khan, F., Yang, M.: Intriguing properties of vision transformers. Adv. Neural. Inf. Process. Syst. 34, 23296–23308 (2021)
Puls, E., Todescato, M., Carbonera, J.: An evaluation of pre-trained models for feature extraction in image classification. arXiv preprint arXiv:2310.02037 (2023)
Maurya, R., Pandey, N., Dutta, M.: VisionCervix: papanicolaou cervical smears classification using novel CNN-Vision ensemble approach. Biomed. Signal Process. Control 79, 104156 (2023)
Chen, H., et al.: Pre-trained image processing transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12299–12310 (2021)
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J’egou, H.: Training data efficient image transformers and distillation through attention. arXiv preprint arXiv:2012.12877 (2020)
Ye, L., Rochan, M., Liu, Z., Wang, Y.: Cross-modal self-attention network for referring image segmentation. In: CVPR (2019)
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. arXiv preprint arXiv:2005.12872 (2020)
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)
Deng, J., Dong, W., Socher, R., Li, Li-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, 5206848 (2009)
Amir, S., Gandelsman, Y., Bagon, S., Dekel, T.: On the effectiveness of ViT features as local semantic descriptors. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022. LNCS, vol. 13804, pp. 39–55. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-25069-9_3
Kataoka, H., Iwata, K., Satoh, Y.: Feature evaluation of deep convolutional neural networks for object recognition and detection. arXiv:1509.07627 (2015)
Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 512–519 (2014)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Computer Vision and Pattern Recognition, pp. 1717–1724 (2014)
Wei, Y., et al.: CNN: single-label to multi-label. Comput. Sci. (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
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Ben-Suliman, K., Krzyżak, A. (2024). Vision Transformer Features-Based Leukemia Classification. In: Suen, C.Y., Krzyzak, A., Ravanelli, M., Trentin, E., Subakan, C., Nobile, N. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2024. Lecture Notes in Computer Science(), vol 15154. Springer, Cham. https://doi.org/10.1007/978-3-031-71602-7_10
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DOI: https://doi.org/10.1007/978-3-031-71602-7_10
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