Deep Neural Network Powered System for Precision Breast Cancer Analysis and Stage Classification
DOI:
https://doi.org/10.29284/f74c6q17Keywords:
Deep Neural Network (DNN), Threshold based segmentation, CLAHE (Contrast Limited Adaptive Histogram Equalization) image Enhancement, Breast cancer prediction and classification,Abstract
The global rise in breast cancer cases has made early diagnosis more important than ever, highlighting the need for fast and dependable detection methods. This study presents a refined Deep Neural Network (DNN) approach intended at taming the identification and classification of breast cancer. The model uses modern machine learning methods to boost both accurateness and efficiency in diagnostic processes. The work provides a detailed look at how DNNs can support breast cancer detection by analyzing the complex visual patterns found in mammogram images. The proposed network architecture is built with multiple layers, each responsible for learning different levels of image features. Convolutional layers extract small, localized patterns, while fully connected layers interpret broader, high-level structures. Together, they enable the model to detect subtle indicators that may signal the presence of tumors. To ensure strong performance across various types of breast tissue and lesion appearances, the network is trained using a variety of datasets. Transfer learning is also incorporated to reduce the difficulties related to limited labeled medical data. By using knowledge from models previously trained on large image collections, the system becomes more adaptable and effective. The model’s presentation is assessed using extensively recognized benchmark datasets and associated with both old techniques and recent deep learning approaches. All overall accuracy and the confusion matrix demonstrate that the proposed DNN achieves high diagnostic reliability. These results propose that the model offers a capable solution for early breast cancer finding and could support more accurate and efficient clinical decision-making.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
