Novel Deep Learning Neural Networks for Breast Cancer Malignancy Estimation

Authors

  • G. Nivedhitha Department of Computer Science & Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India
  • P. Kalpana Department of Computer Science & Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India
  • Rajagopal R Department of Electrical and Electronics Engineering, Francis Xavier Engineering College, Tirunelveli, India
  • Anusha Rani V Department of Electronics and Instrumentation Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India
  • Ajith. B. Singh Department of Computer Science & Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India
  • Sheik Sidthik A. Department of Electrical and Electronics Engineering, VSB College of Engineering & Technical Campus, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org/10.37934/araset.47.1.140151

Keywords:

Artificial Neural Network (ANN), Deep Neural Network (DNN) , Adaptive Moment Estimation (ADAM), Lasso Regression (L1) , Categorical Cross-Entropy (CCE)

Abstract

Medicine has become one of the huge fields which has its own growth each and every day. Few diseases are still a threat to medical field. Because of its destructive nature, cancer is a disease that causes fear in many people throughout the world. However, most cancers are curable if detected early and treated with the right medical care and computer-assisted diagnosis. These days, diagnosis is more popular because it works well as a primary screening test for many diseases, particularly cancer. Deep learning is a method of artificial intelligence where the computer is given intelligence that attempts to emulate the way a human brain thinks. This research focuses on creating a deep neural network that can accurately predict the malignancy of breast cancer up to 98%, allowing medical personnel to diagnose patients more quickly and administer therapy more effectively.

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Author Biographies

G. Nivedhitha, Department of Computer Science & Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India

nivedhithag789975@gmail.com

P. Kalpana, Department of Computer Science & Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India

kalpana321@gmail.com

Rajagopal R, Department of Electrical and Electronics Engineering, Francis Xavier Engineering College, Tirunelveli, India

rajagopalr331@gmail.com

Anusha Rani V , Department of Electronics and Instrumentation Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India

anusharaniv321@gmail.com

Ajith. B. Singh, Department of Computer Science & Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India

ajithbsingh142@gmail.com

Sheik Sidthik A., Department of Electrical and Electronics Engineering, VSB College of Engineering & Technical Campus, Coimbatore, Tamil Nadu, India

sheiksidthik456@gmail.com

Published

2024-06-21

How to Cite

G. Nivedhitha, P. Kalpana, Rajagopal R, , A. R. V., Ajith. B. Singh, & Sheik Sidthik A. (2024). Novel Deep Learning Neural Networks for Breast Cancer Malignancy Estimation. Journal of Advanced Research in Applied Sciences and Engineering Technology, 47(1), 140–151. https://doi.org/10.37934/araset.47.1.140151

Issue

Section

Articles