• Tokachichu Sai Manogyana
• S P Bala Meghana Shivani
• S Pavan Kumar Reddy
• Mrs. T. Veda Reddy

Machine learning (ML) is widely known because of the methodology of choice in carcinoma detection and forecast modeling and its advantages in critical features detection from complex datasets. Several machine learning models are applied to predict whether the diagnosis is malignant or benign when the tumor is found. Cancer is that the second leading reason behind death globally and accounted for 8.8 million deaths in 2015. The first diagnosis and prognosis of a cancer type became a necessity in cancer research, because it can facilitate the next clinical management of patients. For better clinical decisions, it's important to accurately distinguish between benign and malignant tumors. Conventionally, statistical methods are used for the classification of high-risk and low-risk cancer, despite the complex interactions of high-dimensional medical data. To beat the drawbacks of conventional statistical methods, machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support. Deep Neural Networks (DNNs) are being employed here to classify the tumor as benign or malignant. The technique of cancer classification relies on several key differentiators like radius, smoothness, compactness etc. This prediction can help health workers in early detection of cancer type and supply essential diagnosis for it.

malignant tumor, benign tumor, breast cancer, machine learning, neural networks

"Malignant Tumor Cell Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.h62-h71, April-2022, Available :http://www.jetir.org/papers/JETIR2204707.pdf

"Malignant Tumor Cell Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. pph62-h71, April-2022, Available at : http://www.jetir.org/papers/JETIR2204707.pdf