Title: A novel suppressed segmentation framework for hyperspectral image processing in earlier cancer detection

Authors: Kaushal Kishor; Manoj Singhal; Rajesh Kumar Maurya; Pramod Kumar Sagar; Rupak Sharma; Satya Prakash Yadav

Addresses: Department of Information Technology, ABES Institute of Technology, Ghaziabad, Uttar Pradesh, India ' Department of Information Technology, GL Bajaj Institute of Technology and Management (GLBITM), Greater Noida, Uttar Pradesh, India ' Department of Computer Applications, ABES Engineering College, Ghaziabad, Uttar Pradesh, India ' Department of Computer Science and Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad, Uttar Pradesh, India ' Department of Computer Applications, SRM Institute of Science and Technology, NCR Campus, Modinagar, Uttar Pradesh, India ' School of Computer Science Engineering and Technology (SCSET), Bennett University, Greater Noida, Uttar Pradesh, India

Abstract: This paper affords a novel suppressed segmentation framework for Hyperspectral image processing before most cancer detection. This framework integrates the most recent advances in deep learning fashions and image segmentation for the most fulfilling selection-making approximately early cancer analysis. The proposed framework facilitates the fast and correct segmentation of the tumour tissues and other aberrations within hyperspectral images. The key modules of this framework encompass input pre-processing, noisy additive analysis, random area cropping, augmented context representation, hierarchical segmentation, and submit-processing. Experiments performed on real-world datasets show that the proposed framework yields segmentation accuracy similar to other main segmentation techniques while having advanced pace and robustness. The proposed model obtained 95.32% accuracy, 92.89% sensitivity, 91.50% specificity, 94.25% precision and 92.51% F1-score. This proposed method offers an optimised workflow for fast and correct segmentation of tumour tissues in most early cancer diagnoses.

Keywords: image processing; deep learning; suppressed segmentation framework; SSF; hyperspectral image processing; HSIP; earlier cancer detection; traditional imaging techniques; hyperspectral imaging; hierarchical segmentation.

DOI: 10.1504/IJDMB.2026.150964

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.1/2, pp.53 - 89

Received: 02 Dec 2023
Accepted: 15 May 2024
Published online: 06 Jan 2026 *

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