A novel material fabrication to detect breast cancer using deep novel classification mechanism
Jayaraman Venkatesh, Anita Titus, R. Janarthanan, C. Anitha, ChirraKesavaReddy, S. Ranjith · AIP conference proceedings · 2022
Now-a-days, Breast Cancer is a serious cause in world and it raises a death ratio in drastic manner. Most of the women are suffered due to this kind of breast cancer disease, in which several literatures are available regarding this breast cancer identification, but all are strucked over certain level. Lots of researchers in industry providing different digital image processing schemes to identify the Breast Cancer in clear manner to save lives of women but all are strucked with real world prediction scenarios. For Breast Cancer identification mammography is a well-known technique, in which it screens the breast cancer cells in clear manner. However, based on the workload and manual expertise failures, a numerous failures and wrong classification procedures reported as well. So, that a novel computer assisted digital image processing technique and smart tool is required to provide intelligent prediction logics to identify the breast cancer on earlier conditions instead of detecting that in complex stages. In this paper, a novel Deep Learning enabled Image Processing tool is designed, called as Digital Image Processing Scheme enabled SmartKit (DIPSK) to identify the breast cancer disease effectively on earlier stages without any human intervention. The prop osed DIPSB enables the system to acquire the input scanning image and process that with DIP techniques with respect to pre-processing feature extraction and classification. The detailed features are cross-validated with the trained samples, in which the Deep Learning procedure called Improved Convolutional Neural Network (iCNN) is utilized by the SmartKitto provide classification process in accurate manner. The final resulting of this SmartKitis more efficient as compare to the traditional image processing schemes and provides accurate prediction on earlier stages. The resulting section provides the proper proof for the classification accuracy and prediction logics.