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Research Paper | Computers in Biology and Medicine | Iraq | Volume 7 Issue 2, February 2018 | Popularity: 6.8 / 10
Breast Cancer Diagnosis System Based on Nuclei Cells Localization of Fine Needle Biopsies Images
Ali Fawzi Mohammed Ali, Mehdi G. Duaimi
Abstract: Breast cancer is becoming a leading cause of death among women in the whole world, early tumor detection step in the diagnosis stage is obtained by the cytological testing of the breast image mainly based on the cell morphology and architecture distribution. Accurate diagnosis of this disease can ensure a survival of the patients. This paper presents an analysis of digital histopathology Breast cancer based on cytological images of Fine Needle Biopsy (FNB). The main approach of this study is relying on a localization approach for nuclei cell (cell detection). the nuclei are estimated by circular shape using the Circular Hough Transform (CHT). Then, the cells that have been detected by the (CHT) are then filtered to keep only high-quality and accurate cells that have been estimated for further analysis by using a supervised learning approach. In order to filter the nuclei cells and classify the detected circles as correct (cells) or incorrect Support Vector Machine (SVM) as an approach is proposed to use. A set of 25 features were extracted from the remaining filtered nuclei set. (50 features) produced by calculating the mean and variance for each feature. Support Vector Machine (SVM) and Backpropagation Neural Network (BNN) are the two classification algorithms of the biopsies that used in the final stage. The complete diagnostic procedure is tested on total 130 microscopic images of fine needle biopsies obtained from patients and satisfy (99.88 %) classification accuracy by using Resilient Backpropagation Neural Network (RBNN) by selecting only (27 features) from total (50 features). The features selected using mutual information approach which is a distinction between benign or malignant. These results shows that our proposed method consider very promising compared to the previously reported results providing valuable, accurate, and stable diagnostic information.
Keywords: breast cancer, localization, feature extraction, classification
Edition: Volume 7 Issue 2, February 2018
Pages: 1023 - 1032
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