Sunday, May 17, 2020

Essay about Breast Cancer Diagnosis Methods Analysis

Abstract: Breast cancer research over the last decade has been tremendous and the novel methods being develop help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. Some of the studies look very promising and their use in future may decrease the radiation dose to 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. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances give accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.†¦show more content†¦Thus smarter systems are required to decrease the instances of false positives and false negatives. This paper reviews the existing methods some of the methods are yet unproven but the studies look very encouraging. II. LITERATURE SURVEY Mammography is a popular technique but it has its limitations especially in younger women and in denser breasts. The Computer-Aided-Diagnosis has been proposed for the medical prognosis [7-9]. The fuzzy logic and Artificial Neural Network form the basis of the intelligent systems. There are several instances where the artificial intelligence is used for the diagnosis of the breast cancer. The methods have included many Artificial Neural Networks architectures such as Convolution Neural Network [10], Radial Basis Network [11], General Regression Neural Network [11], Probabilistic Neural Network [11], Resilient Back propagation Neural Network [12], and hybrid with Fuzzy Logic [13]. In this paper [7] a supervised artificial neural network [14-16] was used to help classify the breast lesions into malignant and benign classes by processing the computer cytology images. The accuracy of the trained neural network was found to be 82.21%. The ANN has been established as a robust system for th e diagnosis of breast cancer [18].There is a complex relationship between different biomarkers which were identified for the diagnosis of this cancer [19], the MLP neural network was simulated for the diagnosis using four biomarkersShow MoreRelatedThe Assessment Of Nuclear Atypia, Mitotic Count And Lymphocyte Infiltration777 Words   |  4 Pageschemotherapy in high-grade breast cancer) and may have an even larger impact on clinical decisions in the future (e.g., assessment of tumor infiltrating lymphocytes to guide the use of immunotherapies). Thus, the accurate pathological diagnosis represents a critical decision-point in clinical medicine, and there would be tremendous clinical utility to a more objective, reproducible and informative technology for the assessment of breast pathology. 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