Enhancing Breast Cancer Detection: A Machine Learning Approach for Early Diagnosis and Classification

Millions of new cases of breast cancer are discovered worldwide each year, which is a serious health risk. For treatment options to be directed and patient outcomes to be improved, a timely and correct diagnosis is essential. Medical diagnostics has shown machine learning, and more specifically logi...

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Bibliographic Details
Published in2024 11th International Conference on Computing for Sustainable Global Development (INDIACom) pp. 235 - 239
Main Authors Sawant, Aditi, Patil, Divya, Khuman, Dimple, Pingle, Yogesh, Shinde, Vinayak
Format Conference Proceeding
LanguageEnglish
Published Bharati Vidyapeeth, New Delhi 28.02.2024
Subjects
benign
Breast cancer
Breast tissue
early detection
image-based analysis
Logistic regression
Machine learning
malignant
Medical diagnosis
Medical diagnostic imaging
Reliability
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DOI10.23919/INDIACom61295.2024.10498771

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Summary:Millions of new cases of breast cancer are discovered worldwide each year, which is a serious health risk. For treatment options to be directed and patient outcomes to be improved, a timely and correct diagnosis is essential. Medical diagnostics has shown machine learning, and more specifically logistic regression, to be a useful tool. This work uses a logistic regression model to show how machine learning may be used practically to categorize breast cancer cases. The main goal is to develop a model that can reliably classify breast tissue as benign, malignant, or normal based on medical photos in order to aid in the early diagnosis of cancer. By reducing reliance on arbitrary human judgements, this method aims to improve the consistency and effectiveness of the diagnostic procedure.
DOI:10.23919/INDIACom61295.2024.10498771