• Mrs.G.Sharmila
• B.Swathi
• S.Soundarya
• S.Subaranjani

Breast cancer is the most common type of cancer in women and is fatal if not detected and treated early. The cancer cell spreads to other parts of the body and progresses to the next stage if left untreated. This paper presents a machine learning model for the automatic diagnosis of breast cancer. The method used in this paper is DNN as the classification model and a random forest for feature selection. The proposed method detects breast cancer using the machine learning technique Deep Neural Network (DNN) and classification algorithms are used for image and text classifications. We discuss the types of breast cancer tumors and the challenges of early detection to prevent greater harm. Mammography image classification is performed using multi-class image classification. Random Forest (RF) classifiers are used to handle noisy multidimensional data in text classification. The RF model consists of a series of decision trees, each of which is trained on random subsets of features. To derive the most efficient machine learning model, BUI datasets are utilized. The latter approach is particularly interesting because it fits into the growing trend towards personalized predictive medicine. In composing this evaluation, we carried out thorough analysis of the many kinds of machine learning techniques, the types of data involved and the effectiveness of these methods in predicting cancer and prognosis.

Healthcare system, machine learning, breast cancer, deep neural network, cancer diagnosis.

"DETECTION OF BREAST CANCER USING DEEP NEURAL NETWORK (DNN)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.a603-a608, February-2023, Available :http://www.jetir.org/papers/JETIR2302079.pdf

"DETECTION OF BREAST CANCER USING DEEP NEURAL NETWORK (DNN)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppa603-a608, February-2023, Available at : http://www.jetir.org/papers/JETIR2302079.pdf