Next Gen Colposcopy: AI Integration for Cervical Cancer Detection
DOI:
https://doi.org/10.32628/CSEIT25111693Keywords:
Colposcopy, artificial intelligence, deep learning, machine learning, image processing, computer visionAbstract
The proposed work introduces a next-generation colposcopy solution in which artificial intelligence (AI) can be seamlessly integrated into the colposcope instrument itself for early detection of cervical cancer. Unlike traditional approaches that rely on offline analysis, the developed framework enables real-time processing of colposcopic images directly within the clinical workflow. The system incorporates a series of specialized AI modules: a Swin Transformer for specular reflection removal, a Modified Gaussian Mixture Model optimized with the Mexican Axolotl Algorithm for precise segmentation of the cervix, and an EfficientNet-B0 architecture enhanced with hybrid attention for robust classification of lesions. The dataset includes time-series cervigrams captured under saline, acetic acid, and Lugol’s iodine application, along with corresponding clinical metadata to improve diagnostic reliability. Once integrated into the colposcope’s imaging pipeline, each captured image is automatically processed to generate immediate diagnostic insights, assisting clinicians in on-the-spot lesion detection and classification. This embedded design eliminates the need for manual data transfer or external computation, streamlining examinations and reducing subjectivity in interpretation. The proposed AI-powered colposcope represents a clinically adaptable and scalable tool that can significantly improve early detection rates, particularly in high-volume and resource-limited healthcare settings, ultimately supporting better patient outcomes.
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References
L. Mukku and J. Thomas, “Artificial Intelligence in Early Detection of Cervical Intraepithelial Neoplasia.”
J. Thomas and L. Mukku, “AMAA-GMM: Adaptive Mexican Axolotl Algorithm based Enhanced Gaussian Mixture Model to Segment the Cervigram Images,” Int. J. Reason. Intell. Syst., vol. 1, Jan. 2024, doi: 10.1504/IJRIS.2024.10063302. DOI: https://doi.org/10.1504/IJRIS.2024.10063302
L. Mukku and J. Thomas, “CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer,” in International Conference on Intelligent Systems Design and Applications, 2023, pp. 154–163. DOI: https://doi.org/10.1007/978-3-031-64813-7_17
M. Lalasa and J. Thomas, “A Review of Deep Learning Methods in Cervical Cancer Detection,” in International Conference on Soft Computing and Pattern Recognition, 2022, pp. 624–633. DOI: https://doi.org/10.1007/978-3-031-27524-1_60
L. Mukku and J. Thomas, “A machine learning model to predict suicidal tendencies in students,” Asian J. Psychiatr., vol. 79, p. 103363, 2023, doi: https://doi.org/10.1016/j.ajp.2022.103363. DOI: https://doi.org/10.1016/j.ajp.2022.103363
M. R. Lamani, L. Mukku, V. Asha, and K. Padmaja, “A Multimodal Diagnostic Approach,” in Fifth Congress on Intelligent Systems: CIS 2024, Volume 4, 2025, vol. 1278, p. 1.
M. R. Lamani, L. Mukku, V. Asha, and K. Padmaja, “Enhancing Early Detection of Alzheimer’s Disease Through Integrated Deep Learning Models: A Multimodal Diagnostic Approach BT - Fifth Congress on Intelligent Systems,” 2025, pp. 1–11. DOI: https://doi.org/10.1007/978-981-96-2703-5_1
V. Burri and L. Mukku, “Stock Market Prediction Using Machine Learning Techniques,” 2024.
L. Mukku, “Vikas Burri Colorado State University, Fort Collins, Usa.”
L. Mukku and J. Thomas, “Media’s influence on suicide: Building a safer online world for all,” Asian J. Psychiatr., vol. 91, p. 103868, 2024. DOI: https://doi.org/10.1016/j.ajp.2023.103868
S. Mukku, M. Lalasa, K. Nagalakshmamma, G. Savitri, P. Sujata, and K. S. Shanthi Sree, “The role of law and regulation on the intersection of bioprospecting and bio piracy,” in Proceedings of the 2nd International Conference on Computational and Bio Engineering: CBE 2020, 2021, pp. 129–134. DOI: https://doi.org/10.1007/978-981-16-1941-0_14
L. Mukku and J. Thomas, “Comparative Performance Analysis of Deep Learning Models in Cervical Cancer Detection,” in International Conference on Intelligent Systems Design and Applications, 2023, pp. 185–194. DOI: https://doi.org/10.1007/978-3-031-64813-7_20
L. Mukku and J. Thomas, “A Review of Deep Learning Methods in Automatic Facial Micro-expression Recognition,” in International Conference on Computational Intelligence and Data Engineering, 2022, pp. 1–16. DOI: https://doi.org/10.1007/978-981-99-0609-3_1
M. Lalasa, “Threat to wildlife-exploring mass elephant die-offs and role of technology intervention,” SGS-Engineering Sci., vol. 1, no. 01, 2021.
L. Mukku and J. Thomas, “Attention Based Meta-Module to Integrate Cervigrams with Clinical Data for Cervical Cancer Identification,” in International Conference on Intelligent Systems Design and Applications, 2023, pp. 286–295. DOI: https://doi.org/10.1007/978-3-031-64813-7_30
L. Mukku and J. Thomas, “Combining Vision Transformer,” in Fifth Congress on Intelligent Systems: CIS 2024, Volume 2, 2025, vol. 1276, p. 437.
L. Mukku, J. Thomas, R. Vullam, and K. Shoba, “Comparative Performance Analysis of Segmentation Methods in Cervigram Images,” in International Conference on Artificial Intelligence on Textile and Apparel, 2024, pp. 359–370. DOI: https://doi.org/10.1007/978-981-96-1918-4_25
L. Mukku and J. Thomas, “Early-Stage Cervical Cancer Detection via Ensemble Learning and Image Feature Integration,” in International Conference on Intelligent Systems Design and Applications, 2023, pp. 112–122. DOI: https://doi.org/10.1007/978-3-031-64813-7_13
M. Lalasa, S. Nithya, K. Nagalakshmamma, A. Suvarnalatha, and P. Nageshwar Rao, “In Silico Platforms for Systems Toxicology,” in Proceedings of the 2nd International Conference on Computational and Bio Engineering: CBE 2020, 2021, pp. 25–31. DOI: https://doi.org/10.1007/978-981-16-1941-0_4
L. Mukku and V. Burri, “Role of Artificial Intelligence in Cervical Cancer Detection.”
S. Nithya, M. Lalasa, K. Nagalakshmamma, and S. Archana, “Computational approaches in toxicity testing: an overview,” in Advances in Computational and Bio-Engineering: Proceeding of the International Conference on Computational and Bio Engineering, 2019, Volume 2, 2020, pp. 255–261. DOI: https://doi.org/10.1007/978-3-030-46943-6_29
L. Mukku and J. Thomas, “A Specular Reflection Removal Technique in Cervigrams,” in 2023 IEEE International Conference on Contemporary Computing and Communications (InC4), 2023, vol. 1, pp. 1–5. DOI: https://doi.org/10.1109/InC457730.2023.10263224
L. Mukku and J. Thomas, “Specular Reflection Removal Techniques in Cervix Image: A Comprehensive Review,” in International Conference on Emerging Research in Computing, Information, Communication and Applications, 2023, pp. 479–490. DOI: https://doi.org/10.1007/978-981-99-7633-1_36
L. Mukku and J. Thomas, “Advanced Cervical Lesion Detection using Deep Learning Techniques,” in 2024 1st International Conference on Communications and Computer Science (InCCCS), 2024, pp. 1–6. DOI: https://doi.org/10.1109/InCCCS60947.2024.10593544
L. Mukku and J. Thomas, “EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images,” Multimed. Tools Appl., pp. 1–22, 2024. DOI: https://doi.org/10.21203/rs.3.rs-2673502/v1
L. Mukku and J. Thomas, “Deep learning-based cervical lesion segmentation in colposcopic images,” Appl. Eng. Technol., vol. 3, no. 1, pp. 16–25, 2024. DOI: https://doi.org/10.31763/aet.v3i1.1345
L. Mukku and J. Thomas, “A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer,” in Congress on Intelligent Systems, 2023, pp. 63–75. DOI: https://doi.org/10.1007/978-981-99-9037-5_6
L. Mukku and J. Thomas, “TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images,” Int. J. Adv. Intell. Informatics, vol. 9, no. 3, pp. 502–523, 2023. DOI: https://doi.org/10.26555/ijain.v9i3.1431
L. Mukku and J. Thomas, “A Novel Cross-Validation Fusion Model Combining Vision Transformer and DenseNet161 for Enhanced Cervical Lesion Classification,” in Congress on Intelligent Systems, 2024, pp. 437–448. DOI: https://doi.org/10.1007/978-981-96-2697-7_32
L. Mukku and J. Thomas, “CMT-CNN: colposcopic multimodal temporal hybrid deep learning model to detect cervical intraepithelial neoplasia,” Int. J. Adv. Intell. Informatics, vol. 10, no. 2, pp. 317–332, 2024. DOI: https://doi.org/10.26555/ijain.v10i2.1527
L. Mukku and J. Thomas, “Hybrid Decision Fusion based Multimodal Ensemble Framework for Cervical Cancer Detection,” 2023.
L. Mukku and J. Thomas, “Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification,” in Congress on Intelligent Systems, 2023, pp. 109–118. DOI: https://doi.org/10.1007/978-981-99-9043-6_9
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