Citation

Belgaum, Mohammad Riyaz and Momina, Shaik Maryam and Farhath, L. Nousheen and Nikhitha, K. and Naga Jyothi, K. (2023) Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images. In: International Conference on Mining Intelligence and Knowledge Exploration, 28-30 June 2023, Kristiansand, Norway.

Full text not available from this repository.

Abstract

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Dec 2023 07:14
Last Modified:	01 Dec 2023 07:14
URII:	http://shdl.mmu.edu.my/id/eprint/11899

Downloads

Edit (login required)