• B. Srinivas
• Dr. B. Selvapriya
• Dr.C. Berin Jones

Breast cancer remains one of the leading causes of mortality among women worldwide. Early detection significantly improves treatment outcomes and survival rates. This study introduces an advanced deep learning framework incorporating optimized feature selection and enhanced image augmentation techniques to improve early-stage breast cancer detection accuracy. Using a curated dataset of mammographic images, our model applies convolutional neural networks (CNNs) with a novel feature selection algorithm to extract critical patterns, reducing false positives and improving diagnostic precision. The proposed methodology outperforms conventional approaches in terms of accuracy, sensitivity, and specificity, demonstrating its potential for integration into clinical workflows.

Breast Cancer, Deep Learning, Feature Selection, Convolutional Neural Networks, Image Processing, Early Detection

"Enhanced Deep Learning Model for Early Breast Cancer Detection Using Optimized Feature Selection and Medical Imaging Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.f614-f625, September 2023, Available :http://www.jetir.org/papers/JETIR2309690.pdf

"Enhanced Deep Learning Model for Early Breast Cancer Detection Using Optimized Feature Selection and Medical Imaging Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppf614-f625, September 2023, Available at : http://www.jetir.org/papers/JETIR2309690.pdf