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

Diagnostics

Khurram Jawad¹  Mohammad Khalid Imam Rahmani¹  Muhammad Rizwan²  Sadia Safdar²  Abdul Rehman Javed³  Surbhi Bhatia⁴  Thippa Reddy Gadekallu⁵ 

[1] College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia;Department of Computer Science, Kinnaird College for Women, Lahore 44000, Pakistan;Department of Cyber Security, Air University, Islamabad 44000, Pakistan;Department of Information Systems, College of Computer Science & Information Technology, King Faisal University, Hofuf 31982, Saudi Arabia;School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India;

关键词: breast cancer;    computer-aided detection (CAD);    support vector machine (SVM);    K-nearest neighbor (KNN);    machine learning;    deep learning;   

DOI  :  10.3390/diagnostics12051134

来源: DOAJ

【 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.

【 授权许可】

Unknown