Accurate diagnosis of breast cancer is very beneficial as breast cancer is the second-
leading cause of cancer death in women after lung cancer in the US. This study compares …

Background: Early and accurate detection of breast cancer reduces the mortality rate of
breast cancer patients. Decision-making systems based on machine learning and intelligent …

Standard method of assessing breast cancer is a triple test assessment. In this method,
initially a thorough medical examination and patient history is evaluated, secondly imaging …

Background: Breast cancer is one of the most common cancers with a high mortality rate
among women. Prognosis and early diagnosis of breast cancer among women society …

Objectives We propose a deep learning-based ensemble for improving breast cancer
classification and compare it with existing six models including deep neural network on two …

Introduction: It is more than 60 years that Fine NEEDLE ASPIRATION (FNA) has been used
for diagnosing PALPABLE BREAST MASS es and has been known as an effective method …

Breast cancer is the disease that affects women the most worldwide. Due to the
development of computer technology, the efficiency of machine learning has increased, and …

The paper presents an innovative approach for predicting breast cancer by employing fine-
needle aspiration (FNA), the synthetic minority oversampling method (SMOTE), and a Cubic …

유방암은 전 세계적으로 여성들 대다수에게 가장 두려워하는 질환이다. 오늘날 데이터의
증가와 컴퓨팅 기술의 향상으로 머신러닝 (machine learning) 의 효율성이 증대되어 암 검출 및 …

In this work, a hybrid classification system based local database categorization is proposed
for breast cancer classification. The proposed approach aims to improve the classification …