×
Home Current Archive Editorial board
News Contact
Original scientific article

AUTOMATIC SEGMENTATION OF COLON CANCER USING SAM AI

By
B. Senthil Kumar Orcid logo ,
B. Senthil Kumar

St. Joseph’s College of Engineering India

S. Karpagavalli Orcid logo ,
S. Karpagavalli

Emerson Electric Compnay India Private Limited India

K. Keerthana Orcid logo ,
K. Keerthana

Emerson Electric Compnay India Private Limited India

A. Krishnaja Orcid logo
A. Krishnaja

BGR Energy Private Limited India

Abstract

The third most prevalent form of cancer globally in both men and women is colon cancer which affects the digestive tract and occurs in the human body's greater intestine develops from certain polyps are tiisues that grow inside the colon of various sizes which at later stage can develop into a cancer cell. The tissues are taken from colon using biopsy method and cured. Under a microscope, histopathology images are categorised as a manual screening of colon the study tissue. Size of the nucleus and form of the glands are accepted standards for identifying colon cancer cells. The images obtaimed using colonscopy are converted into gray scale images where feature extraction done by conventional techniques and classified. To improve diagnosis methods for processing images and AI characteristics are also used. The automatic thresholding approach known as Otsu's Method, image thresholding, image enhancement, and edge detection techniques are used in the many processing procedures for an image. The SAM AI model is utilised to extract the malignancy characteristic of colon cancer. For the diagnostic images, metrics like resolution and peak signal to noise ratio are acquired

References

1.
Jayaraman S, Esakkirajan S, Veerakumar T. Digital image processing. 2009.
2.
Alzaidi ER. Optimization of Deep Learning Models to Predict Lung Cancer Using Chest X-Ray Images. International Academic Journal of Science and Engineering. 2024;11(1):351–61.
3.
Bangare SL, Dubal A, Bangare PS, Patil S. Reviewing Otsu’s method for image thresholding. International Journal of Applied Engineering Research. 2015;
4.
Gonzalez RE, Woods RC. Digital Image Processing. 2006.
5.
Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, et al. Multi-class texture analysis in colorectal cancer histology. Scientific Reports. 6(1).
6.
Natarajan T, Devan L, Palayanoor Seethapathy R, Balakrishnan SK. A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification. International Journal of Imaging Systems and Technology. 2024;34(2).
7.
Zhang Y, Shen Z, Jiao R. Segment anything model for medical image segmentation: Current applications and future directions. Computers in Biology and Medicine. 2024;171:108238.
8.
Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, et al. Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review. Diagnostics. 12(4):837.
9.
Chaple GN, Daruwala RD, Robert GMSC, Prewitt. Sobel operator based edge detection methods for real time uses on FPGA. In: In2015 international conference on technologies for sustainable development (ICTSD) 2015 Feb 4. p. 1–4.
10.
Vij P, Prashant PM. Analyzing Soil Pollution by Image Processing and Machine Learning at Contaminated Agricultural Field. Natural and Engineering Sciences. 9(2):335–46.
11.
Vij P, Prashant PM. Analyzing Soil Pollution by Image Processing and Machine Learning at Contaminated Agricultural Field. Natural and Engineering Sciences. 9(2):335–46.
12.
In: Proceedings of the international conference on ismac in computational vision and bio-engineering 2018 (ismac-cvb. 2019.
13.
Odeh A, Taleb AA. A Multi-Faceted Encryption Strategy for Securing Patient Information in Medical Imaging. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2023;14(4):164–76.
14.
Hepziba Lizzie S, Senthil Kumar B. Air Quality Prediction Using Time Series Analysis. Advances in Intelligent Systems and Computing. 2021. p. 741–8.
15.
Polo-Generelo S, Rodríguez-Mateo C, Torres B, Pintor-Tortolero J, Guerrero-Martínez JA, König J, et al. Serpine1 mRNA confers mesenchymal characteristics to the cell and promotes CD8+ T cells exclusion from colon adenocarcinomas. Cell Death Discovery. 2024;6;10(1:116.
16.
Akbar B, Gopi VP, Babu VS. Colon cancer detection based on structural and statistical pattern recognition. In: 2015 2nd international conference on electronics and communication systems (ICECS) 2015 Feb 26. p. 1735–9.
17.
Bashier E, Jabeur TB. An efficient secure image encryption algorithm based on total shuffling, integer chaotic maps and median filter. Journal of Internet Services and Information Security. 11(2):46–77.
18.
Siegel RL, Wagle NS, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2023. CA: A Cancer Journal for Clinicians. 2023;73(3):233–54.
19.
Natarajan T, Devan L. Transfer learning supported accurate assessment of multiclass cervix type images. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 2023;237(2):265–81.
20.
Xu L, Walker B, Liang PI, Tong Y, Xu C, Su YC, et al. Colorectal Cancer Detection Based on Deep Learning. Journal of Pathology Informatics. 2020;11(1):28.
21.
Subbaiah S, Agusthiyar R, Kavitha M, Muthukumar VP. ARTIFICIAL INTELLIGENCE FOR OPTIMIZED WELL CONTROL AND MANAGEMENT IN SUBSURFACE MODELS WITH UNPREDICTABLE GEOLOGY. Archives for Technical Sciences. 2024;31(2):140–7.

Citation

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

Article metrics

Google scholar: See link

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.