International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Assistive System to Identify and Manage Lung Cancer

D.A.H.K. RathnayakaDepartment of Information Technology Sri Lanka Institute of Information Technology Malabe, Sri LankaE.M.V.Y. EkanayakeDepartment of Information Technology Sri Lanka Institute of Information Technology Malabe, Sri LankaK.K. ParanavithanaDepartment of Information Technology Sri Lanka Institute of Information Technology Malabe, Sri LankaK.B.A.S.M. DissanayakeDepartment of Information Technology Sri Lanka Institute of Information Technology Malabe, Sri LankaWishalya TisseraDepartment of Information Technology Sri Lanka Institute of Information Technology Malabe, Sri Lanka6Dasuni NawinnaDepartment of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe, Sri Lanka

Vol 7 No 11 (2023): Volume 7, Issue 11, November 2023 | Pages: 79-86

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 08-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.711011

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Abstract

Lung cancer, a leading cause of cancer-related deaths worldwide, necessitates early detection and effective management tools. This research introduces an assistive system leveraging machine learning to identify risk factors and prioritize treatments based on severity. Critical socio-demographic data, like age, gender, and smoking history, enable accurate risk assessment and personalized profiles. The system adapts to lifestyle changes post-diagnosis, offering tailored healthcare solutions. Integrating Mask R-CNN, a deep learning algorithm for medical imaging, enhances lung cancer diagnosis precision and treatment strategies. The user-friendly interface allows healthcare providers, caregivers, and patients easy access via mobile or computer applications. The implementation includes natural language processing for questionnaires and data preprocessing. The system's design consists of three components: front-end interface, back-end server, and Mask R-CNN for tumor identification. Correlation analysis for patient prioritization to generate a list. The proposed system aims to revolutionize lung cancer care, delivering accurate risk assessments, personalized treatment plans, and continuous monitoring, ultimately improving patient outcomes and saving lives.

Keywords

Lung cancer, CT Scan images, Machine Learning, Random Forest Classifier, Natural Language Processing, tokenization, Mask R CNN, Image processing, Logistic Regression


Citation of this Article

D.A.H.K. Rathnayaka, E.M.V.Y. Ekanayake, K.K. Paranavithana, K.B.A.S.M. Dissanayake, Wishalya Tissera, Dasuni Nawinna, “Assistive System to Identify and Manage Lung Cancer” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 11, pp 79-86, November 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.711011

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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