International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | EsoCancer AI: Intelligent Esophageal Adenocarcinoma Detection System Using Deep Learning |
|---|---|
| ABSTRACT | Esophageal adenocarcinoma is one of the most aggressive and rapidly progressing gastrointestinal malignancies, with a poor prognosis when detected at advanced stages. Early detection significantly improves patient survival rates, making accurate and timely diagnosis critical. This paper presents EsoCancer AI, an intelligent medical image analysis platform designed to detect esophageal adenocarcinoma from endoscopic images using deep learning. The proposed system employs a Convolutional Neural Network (CNN) model built using TensorFlow and Keras to analyze endoscopy images and classify them as Normal, Adenocarcinoma, or Barrett's Esophagus. The system automatically preprocesses input images to ensure prediction accuracy across different devices and imaging conditions. In addition to disease detection, EsoCancer AI integrates confidence scoring, probability distribution analysis, and detailed clinical interpretation reports to assist clinicians in diagnosis. The platform includes a secure user authentication system, patient dashboard, and downloadable analysis reports, making it suitable for clinical deployment. The modular architecture consisting of a core diagnostic server ensures scalable performance across web platforms. The proposed system aims to improve early detection rates of esophageal adenocarcinoma, reduce diagnostic errors, and enable clinicians to make data-driven decisions. |
| AUTHOR | NETRAVATI B HIRAGAPPANAVAR, KEERTHANA B M, PRIYA H P, POOJA C A, PROF. ARCHANA K N Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404078 |
| pdf/78_EsoCancer AI Intelligent Esophageal Adenocarcinoma Detection System Using Deep Learning.pdf | |
| KEYWORDS | |
| References | [1] Esteva, A. et al., "Dermatologist-level classification of skin cancer with deep neural networks" Nature, vol. 542, pp. 115-118, 2017. https://doi.org/10.1038/nature21056 [2] Codipilly, D. C. et al., "Artificial intelligence in Barrett's esophagus and esophageal adenocarcinoma: A systematic review," Gastrointestinal Endoscopy,2022. https://doi.org/10.1016/j.gie.2022.01.020 [3] de Groof, A. J. et al., "Deep-learning system detects neoplasia in patients with Barrett's esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking," Gastroenterology, vol. 158, no. 4, pp. 915-929, 2020. https://doi.org/10.1053/j.gastro.2019.11.030 [4] He, K. et al., "Deep residual learning for image recognition," in Proceedings of IEEE CVPR, pp. 770-778, 2016. https://doi.org/10.1109/CVPR.2016.90 [5] Guo, L. et al., "Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model," Nature Communications, vol. 11, 4647, 2020. https://doi.org/10.1038/s41467-020-18407-7 [6] Ebigbo, A. et al., "Computer-aided characterization of Barrett's neoplasia using deep learning: Representing the optimal trade-off between sensitivity and specificity," Endoscopy International Open, vol. 7, pp. E894-E900, 2019. https://doi.org/10.1055/a-0816-4411 [7] Szegedy, C. et al., "Going deeper with convolutions," in Proceedings of IEEE CVPR, 2015. https://doi.org/10.1109/CVPR.2015.7298594 [8] Lecun, Y. et al., "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791 [9] Simonyan, K. and Zisserman, A., "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014. https://arxiv.org/abs/1409.1556 [10] Tajbakhsh, N. et al., "Convolutional neural networks for medical image analysis: Full training or fine tuning?," IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1299-1312, 2016. https://doi.org/10.1109/TMI.2016.2535302 [11] Litjens, G. et al., "A survey on deep learning in medical image analysis," Medical Image Analysis, vol. 42, pp. 60-88, 2017. https://doi.org/10.1016/j.media.2017.07.005 [12] Bray, F. et al., "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394-424, 2018. https://doi.org/10.3322/caac.21492 [13] Ronneberger, O. et al., "U-Net: Convolutional networks for biomedical image segmentation," in Proceedings of MICCAI, pp. 234-241, 2015. https://doi.org/10.1007/978-3-319-24574-4_28 [14] Selvaraju, R. R. et al., "Grad-CAM: Visual explanations from deep networks via gradient-based localization," in Proceedings of ICCV, 2017. https://doi.org/10.1109/ICCV.2017.74 |
