A Cognitive Decision Logic Design for Early-Stage Symptom Analysis and Detection of Cancerous Blood Cell

Deep learning image analysis is considered a smart solution to extract information from biological image data sets. It enables researchers to review the framework between deep learning model and the biological image analysis routine. In this paper, we have gathered blood cancer and healthy blood cell images and symptoms of diseases from web portals to build decision logic. This put forward the subjective research to automate diagnosis processes by extracting the image feature. Image is considered as building bricks for image analysis in deep learning. It entrusts a rousing solution for medical image classification in deep learning and symptom analysis through fuzzy-wuzzy to furnish quick information propagation towards the patient. Eventually the patient can access a quick treatment plan for recovery. A detailed overview of mathematical mechanics of deep learning algorithm and segmentation progress of images are discussed. Deep learning based cancerous cell detection in blood images can point medical experts in the right direction.