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Breast Cancer Melanoma Prediction using Two Layer Deep Neural Network

by Govind Singh, Chetan Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 51
Year of Publication: 2022
Authors: Govind Singh, Chetan Gupta

Govind Singh, Chetan Gupta . Breast Cancer Melanoma Prediction using Two Layer Deep Neural Network. International Journal of Computer Applications. 183, 51 ( Feb 2022), 48-52. DOI=10.5120/ijca2022921933

@article{ 10.5120/ijca2022921933,
author = { Govind Singh, Chetan Gupta },
title = { Breast Cancer Melanoma Prediction using Two Layer Deep Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 51 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 48-52 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2022921933 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:15:37.095360+05:30
%A Govind Singh
%A Chetan Gupta
%T Breast Cancer Melanoma Prediction using Two Layer Deep Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 51
%P 48-52
%D 2022
%I Foundation of Computer Science (FCS), NY, USA

Breast cancer is one of most commonly diagnosed cancer in the world and second most cause of death after lung cancer. The USA cancer society estimate 284200 women will be diagnosed with BC and 44130 will die due to this disease in 2021. The symptoms of breast cancer include a lump in the breast, bloody discharge from the nipple, chronic pain and changes in the shape or texture of the nipple or breast. Generally BC cancer is classified in two class i. Benign ii. Malign through ML techniques to observe the risk of disease for patient. In this time, Machine Learning (ML) techniques are preferred to classify of BC to achieve best efficiency in diagnoses of BC. In this paper, author wants classification of Breast cancer by using the two layers Deep Neural Network with applying two method gradient & back propagation. Neural Network model generates better result 99.45% as compare to NB classifier 96.19% and KNN classifier 97.51% with minimum error.

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Index Terms

Computer Science
Information Sciences


Breast cancer data breast cancer classification deep learning gradient-descent epoch back-propagation accuracy ReLU