International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

Breast Cancer Detection using CNN

Author(s) Ranjeet Yadav, Saurabh Maurya, Shivam Sharma, Sumit Gaurav, Mr. Madhup Agrawal
Country India
Abstract Breast cancer stands as a leading cause of cancer- related fatalities worldwide. Assessing cancer accurately through eosin-stained images remains a complex task, often resulting in discrepancies among medical professionals while reaching a con- clusive diagnosis. To streamline this intricate process, Computer- Aided Diagnosis (CAD) systems present a promising avenue, aiming to reduce costs and enhance efficiency. Traditional clas- sification methods hinge on problem-specific feature extraction, rooted in domain knowledge. However, addressing the multitude of challenges posed by these feature-centric techniques has led to the emergence of deep learning methods as viable alternatives. Here, we propose a novel approach employing Convolutional Neural Networks (CNNs) for the classification of hematoxylin and eosin-stained breast biopsy images. Our method categorizes images into four distinct groups: normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma. Additionally, it per- forms a binary classification distinguishing carcinoma from non- carcinoma cases. The meticulously designed network architecture facilitates information extraction across multiple scales, encom- passing both individual nuclei and overall tissue organization. This design choice enables seamless integration of our proposed system with wholeslide histology images. Notably, our method achieves a commendable accuracy of 77.8four-class classification and demonstrates a high sensitivity of 95.6% in identifying cancer cases.
Keywords Breast cancer detection, Convolutional neural network (CNN), Mammogram images ,Deep learning, Image recognition ,Benign vs. malignant classification ,Early detection ,Diagnosis, Medical imaging ,Feature extraction.
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-26
Cite This Breast Cancer Detection using CNN - Ranjeet Yadav, Saurabh Maurya, Shivam Sharma, Sumit Gaurav, Mr. Madhup Agrawal - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.21254
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.21254
Short DOI https://doi.org/gtwmns

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