Breast Cancer Detection and Classification of Histopathological Images

Breast cancer detection and classification of histopathological images is the standard clinical practice for the diagnosis and prognosis of breast cancer. This paper present the breast cancer detection and classification of benign and malignant breast tumor (Nuclei) based on H & E stained histopathology and feed forward back propagation neural network. Feed forward back propagation neural network classify benign and malignant breast cancer tumor and also classify malignant breast cancer tumor in type1, type2 and type3. Twenty six hundreds set of cell nuclei (tumor) characteristics obtained by applying digital image processing techniques to histopathology images of H & E stained breast biopsy. Feature dataset extracted after breast cancer detection consist of eight features which represent the input layer to the FFN. The FFN will classify input features into benign, malignant and also classify malignant tumor in type1, type2, type3. It can be conclude that FNN gives fast and accurate classification and it works as promising tool for classification of breast cell nuclei. To my best knowledge, there is no existing work that provide breast cancer detection, feature set extraction and classification of benign and malignant breast tumor. Keywords—Colour conversion, Neural Network, Histogram Equalization, Morphological Processing, Segmentation, Histopathology and Breast Cancer.

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