• Mr. N. Rahul Pal
• Mrs.D.Susmitha
• Mrs.R.Gayatri

Breast cancer is one of the most common and rapidly increasing diseases globally. This illness predominantly affects women. Detecting the disease early can help manage breast cancer effectively. A woman’s likelihood of developing breast cancer nearly doubles if she has a first-degree relative—such as a mother, sister, or daughter, who has been diagnosed with it. Early diagnosis of breast cancer through mammogram images is a crucial step in the process of disease detection. Therefore, having effective screening methods is essential for identifying the early signs of breast cancer. Different imaging methods are utilized for screening and diagnosing this illness; the common techniques include mammography, ultrasound, and thermography. Among these, mammography stands out as one of the most crucial methods for the early detection of breast cancer. In light of these concerns, radiography might overlook small tumors, while thermography could prove to be more effective than ultrasound in identifying smaller cancerous lesions. A Convolutional Neural Network (CNN) approach is employed for breast mass detection to reduce the burdens associated with manual assessments. This system leverages several CNN architectures to identify breast cancer and evaluates the outcomes against those obtained from machine learning algorithms.

Breast Cancer, Convolution Neural Network, Mammogram

"A STUDY ON THE DETECTION OF BREAST CANCER USING NEURAL NETWORK WITH CONVOLUTIONAL APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.h624-h636, August-2023, Available :http://www.jetir.org/papers/JETIR2308776.pdf

"A STUDY ON THE DETECTION OF BREAST CANCER USING NEURAL NETWORK WITH CONVOLUTIONAL APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. pph624-h636, August-2023, Available at : http://www.jetir.org/papers/JETIR2308776.pdf