Citation

Hasan, Md Jahid and Ahmad, Wan Siti Halimatul Munirah Wan and Ahmad Fauzi, Mohammad Faizal and Abas, Fazly Salleh (2024) Hybrid Deep Learning Architectures for Histological Image Segmentation. In: 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 19-22 February 2024, Osaka, Japan.

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Abstract

In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (H&E) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Cancer, Histopathology Image, Deep Learning, Nuclei Segmentation, H&E
Subjects:	R Medicine > R Medicine (General) > R855-855.5 Medical technology
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 May 2024 02:31
Last Modified:	03 May 2024 02:31
URII:	http://shdl.mmu.edu.my/id/eprint/12420

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