A comparative knowledge base development for cancerous cell detection based on deep learning and fuzzy computer vision approach

Cancer was once thought to be a chronic fatal disease, but now it is proven to be a myth. This is due to rapid advancements in artificial intelligence (AI) techniques used to detect cancer early by collecting symptoms or analysing cancer images. Various research projects are underway to automate early cancer detection and display a perfect diagnosis plan using AI. Since early accurate diagnosis and detection of cancer disease can increase the survival rate, the present research study aims to build a model equipped with both deep learning and FCVT techniques, so that a comparative analysis between both the techniques for cancer image analysis can be done for deriving the best approximate result before the final decision is taken by the healthcare professionals. The model proposed for analysis is also tested on a standard dataset of cancer cell images and showed 95% accuracy. Hence the present study is done with a hope to design the models so that it can act as an augmentation tool to the existing healthcare facility for cancer disease forecasting and assist clinical oncology domain.