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Tumor Detection Using ML
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I12-1719110
Abstract
Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. Early detection and accurate diagnosis play a crucial role in improving survival rates and enabling timely medical intervention. Traditional diagnostic methods often rely on manual examination and expert interpretation, which can be time-consuming and susceptible to human error. The rapid advancement of Machine Learning (ML) technologies has created new opportunities for developing intelligent systems capable of supporting healthcare professionals in disease diagnosis and clinical decision-making. This paper presents the implementation and deployment of a Web-Based Breast Tumor Detection System that utilizes the Random Forest machine learning algorithm to classify tumors as either benign or malignant. The proposed system is developed using the Wisconsin Breast Cancer Diagnostic Dataset, which contains various tumor-related clinical features such as radius, texture, perimeter, area, smoothness, and symmetry. The collected data undergoes preprocessing steps including feature validation, normalization, and label encoding to improve prediction performance and model reliability.
Keywords
Breast Cancer Detection, Tumor Classification, Machine Learning, Random Forest Classifier, Wisconsin Breast Cancer Dataset, Flask API, Next.js, Healthcare Analytics, Predictive Modeling, Clinical Decision Support System, Data Preprocessing, Web-Based Healthcare Application, Early Cancer Diagnosis, Artificial Intelligence in Healthcare.
How to cite this paper
@article{1719110,
author = {Suyash Dhumne, Shripad Pawar, Datta Rahegaonkar, Chaitanya Hingmire, Dr. Lalit V. Patil},
title = {Tumor Detection Using ML},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {2439-2446},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719110.pdf},
abstract = {Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. Early detection and accurate diagnosis play a crucial role in improving survival rates and enabling timely medical intervention. Traditional diagnostic methods often rely on manual examination and expert interpretation, which can be time-consuming and susceptible to human error. The rapid advancement of Machine Learning (ML) technologies has created new opportunities for developing intelligent systems capable of supporting healthcare professionals in disease diagnosis and clinical decision-making. This paper presents the implementation and deployment of a Web-Based Breast Tumor Detection System that utilizes the Random Forest machine learning algorithm to classify tumors as either benign or malignant. The proposed system is developed using the Wisconsin Breast Cancer Diagnostic Dataset, which contains various tumor-related clinical features such as radius, texture, perimeter, area, smoothness, and symmetry. The collected data undergoes preprocessing steps including feature validation, normalization, and label encoding to improve prediction performance and model reliability.},
keywords = {Breast Cancer Detection, Tumor Classification, Machine Learning, Random Forest Classifier, Wisconsin Breast Cancer Dataset, Flask API, Next.js, Healthcare Analytics, Predictive Modeling, Clinical Decision Support System, Data Preprocessing, Web-Based Healthcare Application, Early Cancer Diagnosis, Artificial Intelligence in Healthcare.},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1719110}
}