Application of Artificial Neural Networks for Early Detection of Breast Cancer

Abstract

Objects placed in mammograms and their features have high diagnostic value. The method of extraction and calculation parameters of the images is a key element in the whole process of assessment of individual medical cases. This applies both to detect the location of microcalcifications, as well as the accurate assessment of the degree of malignancy [1–4, 8, 9]. The analysis of the literature shows that in many cases, to assess them as neural classifiers used neural networks unidirectional scholars algorithm back propagation of error. The large popularity of this type of network stems from the rich knowledge about them supported a wide range of applications [11–13, 17, 18]. This does not mean that this type of network is the best tool for the identification of cancer. In order to verify the correctness of the research, it was decided to compare the two types of neural networks: one-way multi-layer network MLBP [17, 18] and a network of independently building the architecture i.e. Fahlmana network [5, 17]. Both networks were used for the same task, the task of verification and classification of medical data based on the feature vectors. The study attempted to assess the mammograms for detecting the locus and the evaluation grade of microcalcifications [19, 23]. According to the stated objective, the structure of the neural networks were designed so that it was possible to indicate location and performance evaluation grade localized potential microcalcifications. Way of describing mammogram—a set of characteristics and size of the database are the factors that were taken as those that have a key impact on the quality of the diagnostic computer tools [21–23].