Artificial Intelligence in Breast Cancer Early Detection and Diagnosis

2021 ◽  
Author(s):  
Khalid Shaikh ◽  
Sabitha Krishnan ◽  
Rohit Thanki
Author(s):  
Mridul Sharma

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.


2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jinyu Cong ◽  
Benzheng Wei ◽  
Yunlong He ◽  
Yilong Yin ◽  
Yuanjie Zheng

Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.


2013 ◽  
Vol 14 (1) ◽  
pp. 75-80 ◽  
Author(s):  
Palatiyana Vithanage Sajeewanie Chiranthika Vithana ◽  
M.A.Y. Ariyaratne ◽  
P.L. Jayawardana

2018 ◽  
pp. 1-12 ◽  
Author(s):  
Megan Hadley ◽  
Lisa A. Mullen ◽  
Lindsay Dickerson ◽  
Susan C. Harvey

Purpose To assess and develop solutions for an ultrasound-based breast cancer early detection program in rural South Africa 1 year after implementation. Methods A WHO-endorsed RAD-AID Radiology Readiness Assessment was used to evaluate clinic resources. In addition, 5 weeks of observation identified resource deficiencies and reviewed existing documentation methods. On the basis of stakeholders’ input and the BI-RADS, we developed new documentation systems. Training was followed by a survey that assessed feasibility and provider acceptance. Results Resource limitations included lack of computers, unpredictable electrical supply, and inconsistent Internet. The assessment revealed incomplete documentation of breast clinical examinations and history, breast lesions, and follow-up. Furthermore, limitations negatively affected communication among providers. Three solutions were developed: a paper patient history form, a paper clinical findings form, and a computerized patient-tracking data base compliant with BI-RADS. Three nurses, three nursing assistants, and one counselor completed the survey. Seventy-one percent indicated positive general attitudes, and 100% agreed that the documentation system is easy and useful and improves overall quality of care, follow-up, decision making; access to clinical information; and communication between clinicians and patients. Five of the seven providers reported that the system increased visit time, but three of those five believed that the process was valuable. Conclusion Implementation of a breast cancer early detection program in resource-limited regions is challenging, and continual assessment is essential. As a result of identified needs, we developed a documentation system that was broadly accepted. Future steps should focus on increasing efficiency, evaluation of provider attitudes long term, and clinical effect.


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