Programs
- M. Tech. in Automotive Engineering -Postgraduate
- Fellowship in Uro Oncology & Robotic Urology 1 Year -Fellowship
Publication Type : Conference Paper
Publisher : Springer Nature Singapore
Campus : Bengaluru
School : School of Computing
Year : 2025
Abstract : Leukemia, a highly malignant form of blood and bone marrow cancer, can be identified through meticulous examination of blood smears under a microscope. This research is to enhancing the early detection of acute lymphoblastic leukemia (ALL) in patients, thereby enabling prompt and life-saving interventions. The distinctive challenge in identifying ALL lies in the shared traits between lymphocytes and lymphoblasts, notably in the nucleus segmentation stage, a pivotal component of diagnosis. In response to this challenge, we introduce an innovative strategy that employs a CNN-based U-net segmentation model. Our model integrates a pre-trained hybrid feature extractor, seamlessly combining attributes from the VGG19, InceptionResnetV2, and MobilenetV2 architectures. Performance evaluation involves utilizing the openly accessible ALL-IDB2 dataset and employing essential metrics, including the dice coefficient, Jaccard index, and accuracy. Significantly, our approach not only outperforms current state-of-the-art methods such as VGG16, VGG19, Alex Net, Google Net, Squeeze Net, and Xception, but it also achieves a notable 98.4% accuracy rate and an exceptional dice score of 98.9%, underscoring its remarkable superiority.
Cite this Research Publication : G. Niveditha and B. Uma Maheswari, "Acute Lymphoblastic Leukemia Cell Nuclei Segmentation Using U-Net with Ensemble Feature Extractor," in Computational Intelligence in Machine Learning. ICCIML 2023. Lecture Notes in Electrical Engineering, vol. 1400, V. K. Gunjan, A. Kumar, J. M. Zurada, and S. N. Singh, Eds. Singapore: Springer, 2025. doi: 10.1007/978-981-96-4391-2_26