Breast Cancer Detection using Convolutional Neural Network

G Rangandha Rao; Mili Mariya; P. V. S. Pavan Kumar Reddy; P Sudheer; B Karthik Reddy

doi:10.36948/ijfmr.2026.v08i02.72804

Breast Cancer Detection using Convolutional Neural Network

Author(s)	Mr. G Rangandha Rao, Ms. Mili Mariya, Mr. P. V. S. Pavan Kumar Reddy, Mr. P Sudheer, Mr. B Karthik Reddy
Country	India
Abstract	Breast cancer remains one of the leading causes of mortality among women worldwide, accounting for a significant number of deaths each year. The increasing incidence of breast cancer highlights the urgent need for early detection and accurate diagnosis to improve patient outcomes and reduce healthcare burden. Traditional diagnostic methods often rely on manual interpretation of medical images, expert analysis, and time-consuming procedures, which may delay timely treatment. In this context, the integration of deep learning techniques into healthcare systems offers a promising solution for efficient and rapid disease detection. This paper presents the design and implementation of a breast cancer detection system using Convolutional Neural Networks (CNN), a powerful deep learning approach for image classification. The proposed system utilizes ultrasound images and classifies them into three categories: Normal, Benign, and Malignant. The model automatically extracts relevant features from medical images, enabling accurate detection without the need for manual feature engineering. The dataset used in this study consists of labeled ultrasound images obtained from reliable medical sources, ensuring consistency and effectiveness in evaluation. Prior to model training, the data undergoes preprocessing steps such as resizing, normalization, and data augmentation to enhance model performance and generalization. The dataset is then divided into training and testing subsets to validate the effectiveness of the proposed approach. The CNN model is selected due to its ability to learn complex patterns and hierarchical features from image data. The architecture includes convolutional, pooling, and fully connected layers, enabling efficient feature extraction and classification. The performance of the model is evaluated using standard metrics such as accuracy, precision, recall, and F1-score, providing a comprehensive assessment of its predictive capability. Experimental results demonstrate that the proposed system achieves high accuracy and reliable performance in detecting breast cancer from ultrasound images. The model shows strong potential in assisting healthcare professionals by providing fast and accurate diagnostic support. Additionally, the system is designed with scalability and ease of use in mind, making it suitable for real-time applications and integration into web-based healthcare platforms. In conclusion, the CNN-based breast cancer detection system provides an efficient, scalable, and cost-effective solution for early diagnosis. This approach can significantly contribute to improving patient outcomes by enabling timely detection and supporting clinical decision-making in modern healthcare systems.
Keywords	Breast Cancer Detection, Convolutional Neural Network (CNN), Deep Learning, Ultrasound Imaging, Medical Image Analysis, ComputerAided Diagnosis (CAD), Image Classification, Predictive Modeling, Healthcare Analytics, Feature Extraction, Supervised Learning, Clinical Decision Support System
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-29
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.72804

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Breast Cancer Detection using Convolutional Neural Network

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