A Data-Driven Machine Learning Framework for Early Detection and Accurate Diagnosis of Breast Cancer

Saurav Kumar, Sakshi Singh, Nikhat Akhtar and Yusuf Perwej

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 3, 268-283

Abstract: Breast cancer is a frequent cause of death among women in poor nations. Timely diagnosis and treatment are critical for favorable results. Breast cancer is a disease in which breast cells grow and is regarded a primary cause of mortality in women. This illness has two subgroups namely; invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). Advances in AI (artificial intelligence) and ML (machine learning) approaches have led to the development of more accurate and reliable models for detecting and treating this condition. Convolutional neural networks (CNNs) are useful in breast cancer diagnosis and prevention. Proper classification of patients may prevent unneeded therapies. Machine learning (ML) is one of the main tools of modern medical imaging research. Machine learning based data categorization approaches are efficient. Especially in the field of medicine where such approaches are very often used during diagnosis and analysis for decision making. In this case, we use the attributes provided by the data to apply several machine learning methods to predict whether a tumor is benign or malignant. Convolutional neural networks (CNNs) are useful in breast cancer diagnosis and prevention. In this article we describe a system for the identification of breast cancer and address the potential of machine learning (ML) algorithms to improve the early detection and diagnosis of breast cancer.

Keywords: Machine Learning; Breast Cancer; Ultrasound Images; Pre-processing; Classification; Convolutional Neural Network (CNN); UCI Machine Learning Repository (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26123315
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v12:y2026:i3:id:2016

DOI: 10.32628/CSEIT26123315

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