Title: A novel system for early detection of breast cancer using area and entropy features of malignant tumour
Authors: M. Varalatchoumy; M. Ravishankar
Addresses: Department of ISE, BNMIT, India ' Vidya Vikas Institute of Technology, Mysore, India
Abstract: Computer aided detection and classification system has been developed to detect breast cancer at an early stage by predicting the area and texture of malignant tumours. Noise removal and image enhancement is carried out in the pre-processing stage by using adaptive median filter and contrast limited histogram equalisation techniques. Improved watershed segmentation technique with appropriate internal and external markers, have proved to be an efficient approach in detecting the region of interest. The detected tumours are classified using feedforward artificial neural network that are trained using textural features. Area and entropy features extracted from malignant tumours aids in early detection of breast cancer by categorising malignant tumours as belonging to stage I or stage II. The overall efficiency of the system, for identifying stages of malignant tumour is 92%, which has been identified to be high when compared to all existing systems. Mammogram images from Mammographic Image Analysis Society (MIAS) database was used for training the system and efficiency of the system was tested using real time hospital images.
Keywords: malignant tumour; computer aided diagnostic system; CAD system; adaptive median filter; CLAHE; watershed segmentation; internal and external markers; textural features; artificial neural network; ANN; area and entropy of malignant tumour; stage of breast cancer.
International Journal of Advanced Intelligence Paradigms, 2020 Vol.16 No.3/4, pp.355 - 367
Received: 02 Jun 2017
Accepted: 06 Oct 2017
Published online: 01 Jun 2020 *