Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection

被引:26
作者
Safdar, Sadia [1 ]
Rizwan, Muhammad [1 ]
Gadekallu, Thippa Reddy [2 ]
Javed, Abdul Rehman [3 ]
Rahmani, Mohammad Khalid Imam [4 ]
Jawad, Khurram [4 ]
Bhatia, Surbhi [5 ]
机构
[1] Kinnaird Coll Women, Dept Comp Sci, Lahore 44000, Pakistan
[2] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India
[3] Air Univ, Dept Cyber Secur, Islamabad 44000, Pakistan
[4] Saudi Elect Univ, Coll Comp & Informat, Riyadh 11673, Saudi Arabia
[5] King Faisal Univ, Coll Comp Sci & Informat Technol, Dept Informat Syst, Al Hufuf 31982, Saudi Arabia
关键词
breast cancer; computer-aided detection (CAD); support vector machine (SVM); K-nearest neighbor (KNN); machine learning; deep learning; CLASSIFICATION;
D O I
10.3390/diagnostics12051134
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing survival rates against this disease. A computer-aided detection (CAD) system is necessary for radiologists to differentiate between normal and abnormal cell growth. This research consists of two parts; the first part involves a brief overview of the different image modalities, using a wide range of research databases to source information such as ultrasound, histography, and mammography to access various publications. The second part evaluates different machine learning techniques used to estimate breast cancer recurrence rates. The first step is to perform preprocessing, including eliminating missing values, data noise, and transformation. The dataset is divided as follows: 60% of the dataset is used for training, and the rest, 40%, is used for testing. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity. Our proposed model uses machine learning techniques such as support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN) to achieve better accuracy in breast cancer classification. Furthermore, we attain the highest accuracy of 97.7% with 0.01 FPR, 0.03 FNR, and an area under the ROC curve (AUC) score of 0.99. The results show that our proposed model successfully classifies breast tumors while overcoming previous research limitations. Finally, we summarize the paper with the future trends and challenges of the classification and segmentation in breast cancer detection.
引用
收藏
页数:18
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