• Neeti Arora

This fragment offering the obtained results ofthe input dataset, flow/system model, image pre-processing techniques, image feature extraction technique, machine learning, and performance evaluation metrics. The primary goal is to be classifying the breast cancer from the huge medical database based on the density level then the severity level has been observed like normal, moderate and severe. In order to identify the non-cancerous and cancerous image, we used texture and edge based segmentation approach

Image processing techniques, extraction technique, segmentation approach.

"Outcomes: Development and Execution – Hybrid Segmentation and Mammogram Classification for Detection and Prevention of Cancer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 7, page no.29-45, July-2020, Available :http://www.jetir.org/papers/JETIR2007304.pdf

"Outcomes: Development and Execution – Hybrid Segmentation and Mammogram Classification for Detection and Prevention of Cancer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 7, page no. pp29-45, July-2020, Available at : http://www.jetir.org/papers/JETIR2007304.pdf