Empowering Early Diagnosis - Leveraging Machine Learning for Breast Cancer Detection

Aug 2, 2026

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Breast cancer remains a leading cause of mortality among women, making early identification and prompt treatment critical to improving clinical outcomes. This review examines how machine learning approaches analyze mammograms and other medical data to detect malignant patterns with encouraging performance. It surveys key models, including neural networks, support vector machines, random forests, and deep learning, while addressing practical challenges such as dataset quality, generalization, and validation rigor, and highlighting opportunities for future research.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Introduction

Limitations of conventional mammography

Machine learning approaches and data sources

Key challenges in reliable model development

Overview of machine learning models

Artificial neural networks

Support vector machines

Random forests

Deep learning

Importance of early diagnosis and future directions

IJRECE VOL. 11 ISSUE 2 APR-JUNE 2023 ISSN: 2393-9028 (PRINT) | ISSN: 2348-2281 (ONLINE) Empowering Early Diagnosis: Leveraging Machine Learning for Breast Cancer Detection Sk. Nazma Sultana 1, V. Nagi Reddy2, Bandlamudi Bhavya3, Katragadda Mytheri4 1, 2, 3, 4 Department of Information Technology and computer Applications, VFSTR Deemed to be University, Guntur, A.P., India ABSTRACT-Worldwide, breast cancer ranks as the second greatest cause of mortality for women. Research into breast cancer detection is essential due to the positive impact early identification and prompt treatment may have on patient outcomes. Analysis of mammograms and other medical data using machine learning algorithms has shown encouraging results in the identification of breast cancer. In this study, we survey the current top methods for detecting breast cancer via machine learning. In this article, we'll go through the many machine learning models used for breast cancer diagnosis, as well as the difficulties inherent in creating models that can be relied upon to accurately diagnose the disease. In addition, we point out the potential benefits of machine learning in breast cancer screening and diagnosis and suggest new avenues for study. Keywords: Breast cancer detection, machine learning, mammogram images, artificial neural networks, support vector machines, random forests, deep learning. I. INTRODUCTION Millions of women throughout the world are affected by breast cancer, making it a major public health issue. Traditional screening procedures like mammography have limits in accuracy and reliability that prevent them from being fully effective in their mission to improve patient outcomes through early identification and treatment. The large amounts of data produced by medical imaging and other diagnostic equipment have sparked a renewed interest in using machine learning methods to the identification of breast cancer in recent years. Mammogram pictures, genetic data, and other medical information may be used to train machine learning algorithms to recognise patterns and traits, allowing for more precise and expedited breast cancer detection. This study provides a thorough review of the various models and algorithms currently in use for breast cancer screening using machine learning approaches. We explore the potential influence of machine learning in breast cancer screening and diagnosis, as well as the obstacles and possibilities involved with this approach. Finally, we outline potential avenues for further study in this field, stressing the importance of highquality data, strong models, and sound validation techniques. Breast cancer is the most frequent disease in American women and accounts for around 25% of all cancer diagnoses in women globally. There has been increasing focus in recent years on using machine learning methods to the identification and diagnosis of breast cancer. Mammograms, genetic information, and other medical data may all be used to train machine learning algorithms to look for indicators of breast cancer. There are limitations to the accuracy and reliability of conventional screening procedures like mammography. Diagnostic errors, both positive and negative, can negatively affect patient outcomes by delaying treatment and causing unneeded operations. Using the massive volumes of data produced by medical imaging and other diagnostic instruments, machine learning has the potential to overcome these constraints. Artificial neural networks (ANNs), support vector machines (SVMs), random forests, and deep learning models are only some of the machine learning methods that have been applied to the problem of detecting breast cancer. To distinguish between cancerous and noncancerous tumours, these methods may be trained on massive datasets of mammography images and other medical data. Once these models have been trained, they may be used to reliably categorise new instances as malignant or benign. However, it is d

FAQs

Why is early diagnosis important for breast cancer outcomes?

Early diagnosis enables prompt treatment, which can substantially improve patient outcomes and reduce delays caused by diagnostic errors.

How can machine learning support breast cancer detection?

Machine learning models can be trained on mammogram images and other medical data to recognize malignant versus benign patterns and accelerate more precise detection.

What are the main challenges when building reliable machine learning models?

Reliable models require comprehensive datasets covering tumor types and stages, and they must be tested on separate datasets to ensure generalization to new cases.

Aug 2, 2026

Upload by Quinn