Abstract
Colon nuclei detection within Haematoxylin & Eosin (H &E) stained histology images is important to mitigate abnormalities or diseases like colon cancer in its early stages. Therefore, the objective of the proposed work is to perform the colon nuclei segmentation, classification, and counting of the nuclei or cellular composition. This paper presents a hybrid deep learning model that combines deep-learned features obtained from the ResNet50-based model with the handcrafted features. The proposed work uses the horizontal and vertical net (HoVer-Net) as baseline model presented by the CoNIC2022 challenge team and modified it to incorporate handcrafted features obtained using two feature descriptors such as local binary patterns and the histogram of oriented gradients. The proposed model is trained and validated using the CoNIC2022 dataset. The proposed model shows a significant improvement over the baseline HoVer-Net model in segmentation and classification as well as nuclei counting tasks. The proposed work demonstrates the usefulness of combining deep features with the handcrafted features in the colon nuclei identification task.
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Dumbhare, P., Dubey, Y., Phuse, V., Jamthikar, A., Padole, H., Gupta, D. (2023). Combining Deep-Learned and Hand-Crafted Features for Segmentation, Classification and Counting of Colon Nuclei in H &E Stained Histology Images. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_52
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