ARCHIVES
Original Article
AI-Based Stomach Cancer Detection Using Biomarkers, Medical Images, and Voice Analysis
Shafiulla Sharif1
Sameer B2
Girish Chougule3
Devesh H M4
Praveen Kumar T S5
Sharanya H V6
1 2 Assistant Professor, Department of Computer Science Engineering, Jain Institute of Technology Davanagere, Karnataka, India. 3 4 5 6 4th Year B.E, Department of Computer Science Engineering, Jain Institute of Technology Davanagere, Karnataka, India.
Published Online: January-February 2026
Pages: 01-08
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260701001References
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Expert Syst., 40, e13311 (2023).[30]. U. Khan, S. Paheding, C.P. Elkin, V.K. Devabhaktuni, “Trends in deep learning for medical hyperspectral image analysis,” IEEE
Access, 9, 79534–79548 (2021).
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arXiv:1911.03584 (2019).
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arXiv:2010.11929 (2020).
[33]. S. Trajanovski, C. Shan, P.J. Weijtmans, S.G.B. de Koning, T.J. Ruers, “Tongue tumor detection in hyperspectral images using deep
learning semantic segmentation,” IEEE Trans. Biomed. Eng., 68, 1330–1340 (2020).
[34]. F.B. Muniz, M. d F.O. Baffa, S.B. Garcia, L. Bachmann, J.C. Felipe, “Histopathological diagnosis of colon cancer using micro-FTIR
hyper spectral imaging and deep learning,” Comput. Methods Prog. Biomed., 231, 107388 (2023).
[35]. I.A. Cruz-Guerrero et al., “Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an
ensemble of deep neural networks,” Healthc. Technol. Lett., 11, 240–251 (2024).
[36]. B.J. Tromberg et al., “Non-invasive in vivo characterization of breast tumors using photon migration spectroscopy,” Neoplasia, 2,
26–40 (2000)
enhanced Raman scattering Sensors distinguishes early and advanced gastric cancer patients from healthy persons,” ACS Nano.
[2]. K.K. Farman Farma, N. Mahdavi far, S. Hassanipour, H. Salehiniya, “Epidemiologic study of gastric cancer in Iran: A systematic
review,” Clin. Exp. Gastroenterol..
[3]. A. Jemal et al., “Global cancer statistics,” CA Cancer J. Clin., 61, 69–90, https://doi.org/10.3322/caac.20107, Erratum in: CA Cancer
J. Clin. 61, 134 (2011).
[4]. “GLOBOCAN 2012: Estimated cancer incidence, mortality and prevalence worldwide.”
[5]. M. Mahmood, B. Al-Khateeb, W.M. Alwash, “A review on neural networks approach on classifying cancers,” Int. J. Artif. Intell.,
9(2), 317–326 (2020).
[6]. A.M. Brushfield, T.T. Luu, B.D. Callahan, P.E. Gilbert, “A comparison of discrimination and reversal learning for olfactory and
visual stimuli in aged rats,” Behav. Neurosci., 122(1), 54–62 (2008).
[7]. H. Sung et al., “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185
countries,” CA: a Cancer J. Clin., 71, 209–249 (2021).
[8]. E.C. Smyth, M. Nilsson, H.I. Grabsch, N.C. van Grieken, F. Lordick, “Gastric cancer,” Lancet, 396, 635–648 (2020).
[9]. K.S. Choi et al., “Effect of endoscopy screening on stage at gastric cancer diagnosis: results of the National Cancer Screening
Programme in Korea,” Br. J. Cancer, 112, 608–612 (2015).
[10]. X. Zhang et al., “Endoscopic screening in Asian countries is associated with reduced gastric cancer mortality: a meta-analysis and
systematic review,” Gastroenterology, 155, 347–354.e9 (2018).
[11]. J.K. Jun et al., “Effectiveness of the Korean National Cancer Screening Program in reducing gastric cancer mortality,”
Gastroenterology, 152, 1319–1328.e7 (2017).
[12]. Japanese Gastric Cancer Association, “Japanese gastric cancer treatment guidelines 2021,” Gastric Cancer, 26, 1–25 (2023).
[13]. H. Isomoto et al., “Endoscopic submucosal dissection for early gastric cancer: a large-scale feasibility study,” Gut, 58, 331–336
(2009).
[14]. S.C. Shah, M.B. Piazuelo, E.J. Kuipers, D. Li, “AGA clinical practice update on the diagnosis and management of atrophic gastritis:
expert review,” Gastroenterology, 161, 1325–1332.e7 (2021).
[15]. D.L. Snyder, A.M. Hammoud, R.L. White, “Image recovery from data acquired with a charge-coupled-device camera,” JOSA A, 10,
1014–1023 (1993).
[16]. K. Yao, T. Yao, T. Matsui, A. Iwashita, T. Oishi, “Hemoglobin content in intramucosal gastric carcinoma as a marker of histologic
differentiation: a clinical application of quantitative electronic endoscopy,” Gastrointest. Endosc., 52, 241–245 (2000).
[17]. N. Uedo et al., “Differences in routine esophagogastroduodenoscopy between Japanese and international facilities: a questionnaire
survey,” Dig. Endosc., 28, 16–24 (2016).
[18]. K. Yao et al., “Guidelines for endoscopic diagnosis of early gastric cancer,” Dig. Endosc., 32, 663–698 (2020).
[19]. H. Doyama, H. Nakanishi, K. Yao, “Image-enhanced endoscopy and its corresponding histopathology in the stomach,” Gut Liver,
15, 329 (2021).
[20]. Y.T. Lee et al., “EUS-guided injection of cyanoacrylate for bleeding gastric varices,” Gastrointest. Endosc., 52, 168–174 (2000).
[21]. J.M. Kim et al., “Pre-and post-ESD discrepancies in clinicopathologic criteria in early gastric cancer: the NECA–Korea ESD for Early
Gastric Cancer Prospective Study (N-Keep),” Gastric Cancer, 19, 1104–1113 (2016).
[22]. A. Cerussi et al., “In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband
diffuse optical spectroscopy,” J. Biomed. Opt., 11 (2006).
[23]. D.J. Waterhouse, C.R. Fitzpatrick, B.W. Pogue, J.P. O’Connor, S.E. Bohndiek, “A roadmap for the clinical implementation of optical
imaging biomarkers,” Nat. Biomed. Eng., 3, 339–353 (2019).
[24]. J. Yoon, “Hyperspectral imaging for clinical applications,” BioChip J., 16, 1–12 (2022).
[25]. G. Lu, B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt., 19, 010901 (2014).
[26]. J. Yoon et al., “A clinically translatable hyperspectral endoscopy (HySE) system for imaging the gastrointestinal tract,” Nat.
Commun., 10, 1902 (2019).
[27]. J. Yoon et al., “First experience in clinical application of hyperspec tral endoscopy for evaluation of colonic polyps,” J. Biophotonics,
14, e202100078 (2021).
[28]. R. Leon et al., “VNIR–NIR hyperspectral imaging fusion targeting intraoperative brain cancer detection,” Sci. Rep., 11, 19696 (2021).
[29]. H. Mangotra, S. Srivastava, G. Jaiswal, R. Rani, A. Sharma, “Hyper spectral imaging for early diagnosis of diseases: a review,”
Expert Syst., 40, e13311 (2023).[30]. U. Khan, S. Paheding, C.P. Elkin, V.K. Devabhaktuni, “Trends in deep learning for medical hyperspectral image analysis,” IEEE
Access, 9, 79534–79548 (2021).
[31]. J. Cordonnier, A. Loukas, M. Jaggi, “On the relationship between self attention and convolutional layers,” arXiv preprint
arXiv:1911.03584 (2019).
[32]. A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint
arXiv:2010.11929 (2020).
[33]. S. Trajanovski, C. Shan, P.J. Weijtmans, S.G.B. de Koning, T.J. Ruers, “Tongue tumor detection in hyperspectral images using deep
learning semantic segmentation,” IEEE Trans. Biomed. Eng., 68, 1330–1340 (2020).
[34]. F.B. Muniz, M. d F.O. Baffa, S.B. Garcia, L. Bachmann, J.C. Felipe, “Histopathological diagnosis of colon cancer using micro-FTIR
hyper spectral imaging and deep learning,” Comput. Methods Prog. Biomed., 231, 107388 (2023).
[35]. I.A. Cruz-Guerrero et al., “Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an
ensemble of deep neural networks,” Healthc. Technol. Lett., 11, 240–251 (2024).
[36]. B.J. Tromberg et al., “Non-invasive in vivo characterization of breast tumors using photon migration spectroscopy,” Neoplasia, 2,
26–40 (2000)
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