Deep Neural Network Design and Implementation for Predicting Malignancy of Cancer Tumors
Javvaji Geetha Priya1, Koppisetti Sri Satyanjani2, K Amrendra3, Kolla Bhanu Prakash4
1Javvaji Geetha Priya, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
2Koppisetti Sri Satyanjani, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
3K Amarendra, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
4Kolla Bhanu Prakash, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7845-7847 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5399118419/2019©BEIESP | DOI: 10.35940/ijrte.D5399.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer is the most frequent cancer among women after lung cancer. It is second popular cause of death in the world. Breast cancer cells usually form a tumor (abnormal tissue).There are two types of breast cancer tumors: those that are non-cancerous, or ‘benign’, and those that are cancerous, which are ‘malignant’. Early detection of cancer tumors can prevent its mortality and can take respective precautions according to the type of cancer tumor (either it is benign or malignant). The main objective of this paper is to early diagnosis of the malignancy of cancer tumors which helps to decrease the death rate and helps to give more lives. The selection of suitable deep learning technique is a challenge for the diagnosis of breast cancer. So we created model for prediction of malignancy of cancer tumors using neural network to analyze risk levels that helps in prognosis. It is useful for a doctor to predict the stage of cancer and take respective precautions.
Keywords: Malignancy, Benign, Neural, Network, Cancer, Random Forest, Classification.
Scope of the Article: Classification.