Affiliations: [a] Department of Computer Applications, University College of Engineering, Anna University, BIT Campus, Chennai, India
| [b] Department of Computer Applications, Anna University, Chennai, BIT Campus, Tiruchirappalli, Tamil Nadu
Correspondence:
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Corresponding author. S. Parvathi, Department of Computer Applications, University College of Engineering, Anna University, BIT Campus, Chennai, India. E-mail: [email protected].
Abstract: Breast cancer is considered as a most dangerous type of cancer found in women among all the cancers. Around 2.3 million women in the world are affected by this cancer and there is no cure if it is left untreated at an earlier stage. Therefore, early diagnosis of this disease is an important consideration to save the life of millions of women. Many machine learning models have been evolved in the recent years for breast cancer detection. However, all the currently available works focused only on improving the prediction accuracy, they need more attention on providing reliable services. This work presents an efficient breast cancer detection mechanism using deep learning strategies. The various assortments like breast image shapes, the intensity of images, regions of an image, illuminations, and contrast are the conceivable factors that define breast cancer identification. This study offers a strong image detection process for breast cancer mammography images by considering the whole slide image. Here, the input process for the preprocessing stage will remove the noise present in the image using Gaussian Filter (GF). The preprocessed image moves to the image segmentation and then forward to the feature extraction for extracting the features of the images using Cauchy distribution-based segmentation and Shearlet based feature extraction. Then the specialized features can be isolated using the Entropy PCA based feature selection. Finally, the breast cancer area is to be detected as benign or malignant accurately by using the Unified probability with LSTM neural network classification (UP-LSTM) for whole slide image (WSI). The attained outcomes and the detected outcomes were stored in cloud using a security mechanism for further monitoring purposes. To provide an efficient security, a Bio-inspired Iterative Honey Bee (BI-IHB) encryption is employed which is decrypted on user request. The reliability of the stored data is then found using FMEA (Failure mode and effective analysis) approach. From the experimental analysis, it is observed that UP-LSTM classifier model offers accuracy of 99.26% , sensitivity of 100% , and precision value of 98.59% which is better than the other state of the art techniques.
Keywords: Breast cancer, Gaussian Filter, Cauchy distribution-based segmentation, Shearlet based feature extraction, Entropy PCA based feature selection, unified probability with LSTM neural network classification, FMEA (failure mode and effective analysis), benign, malignant, histopathology images, WSI, cloud security, bio-inspired iterative honey bee (BI-IHB).
