An Analysis of the Methods Employed for Breast Cancer Diagnosis
نویسندگان
چکیده
Breast cancer research over the last decade has been tremendous. The ground breaking innovations and novel methods help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. The prediction of the recurrent cancer is also crucial for the survival of the patient. This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. Some of the methods are yet unproven but the studies look very encouraging. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.
منابع مشابه
Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets
Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...
متن کاملDesigning and validating an information management software for breast cancer treatment
Background and Aim: Breast cancer is the most common type of cancer in Iran and around the world. It has been recognized as the most significant cause of cancer deaths in developing countries such as Iran. Considering the necessity of accurate and timely diagnosis of this disease, the aim of this research was to design and validate an information management software for the treatment of breast ...
متن کاملBreast Cancer Diagnosis from Perspective of Class Imbalance
Introduction: Breast cancer is the second cause of mortality among women. Early detection is the only rescue to reduce the risk of breast cancer mortality. Traditional methods cannot effectively diagnose tumor since they are based on the assumption of well-balanced dataset.. However, a hybrid method can help to alleviate the two-class imbalance problem existing in the ...
متن کاملAn Ensemble Classification Model for the Diagnosis of Breast Cancer Using Stacked Generalization
Introduction: Breast cancer is one of the most common types of cancer whose incidence has increased dramatically in recent years. In order to diagnose this disease, many parameters must be taken into consideration and mistakes are possible due to human errors or environmental factors. For this reason, in recent decades, Artificial Intelligence has been used by medical practitioners to diagnose ...
متن کاملAn Ensemble Classification Model for the Diagnosis of Breast Cancer Using Stacked Generalization
Introduction: Breast cancer is one of the most common types of cancer whose incidence has increased dramatically in recent years. In order to diagnose this disease, many parameters must be taken into consideration and mistakes are possible due to human errors or environmental factors. For this reason, in recent decades, Artificial Intelligence has been used by medical practitioners to diagnose ...
متن کاملApplying Two Computational Classification Methods to Predict the Risk of Breast Cancer: A Comparative Study
Introduction: Lack of a proper method for early detection and diagnostic errors in medicine are some fundamental problems in treating cancer. Data analysis techniques may significantly help early diagnosis. The current study aimed at applying and evaluating neural networks and decision tree algorithm on breast cancer patients’ data for early cancer prediction. Methods: In the current stu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1206.3777 شماره
صفحات -
تاریخ انتشار 2012