Diagnosis of Breast Cancer Based on Support Vector Machine and Random Forest Methods

Wu, Yuyao

doi:10.1109/cds49703.2020.00036

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…After a number of tests and parameter adjustments based on theoretical knowledge and industry standards, the best classifier was found. A table for each model contains the experimental results of a breast cancer diagnosis system using neural network models, with the best parameter values selected by experiment [5]. The most effective network parameters were found to be the learning rate, number of hidden layers, and hidden layer neurons.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Novel Deep Learning Neural Networks for Breast Cancer Malignancy Estimation

G. Nivedhitha,

P. Kalpana,

Rajagopal R

et al. 2024

ARASET

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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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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%