Fuzzy Logic Systems for Diagnosis of Renal Cancer

Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stages. In this research paper, an intelligent medical diagnostic system to diagnose renal cancer is developed by using fuzzy and neuro-fuzzy techniques. Essentially, for a fuzzy inference system, two layers are used. The first layer gives the output about whether the patient is having renal cancer or not. Similarly, the second layer detects the current stage of suffering patients. While in the development of a medical diagnostic system by using a neuro-fuzzy technique, the Gaussian membership functions are used for all the input variables considered for the diagnosis. In this paper, the comparison between the performance of developed systems has been done by taking some suitable parameters. The results obtained from this comparison study show that the intelligent medical system developed by using a neuro-fuzzy model gives the more precise and accurate results than existing systems.

Fuzzy Deep Medical Diagnostic System: Gray Relation Framework and the Guiding Functionalities for the Professional Sports Club Social Responsibility

Fuzzy deep medical diagnostic system based on gray relation framework and the guiding functionalities for the professional sports club social responsibility is proposed in this paper. Medical high-tech has two features, namely formal logic and mathematics. That is to say, they use formal logic to build the theoretical system, which requires that the principles of the medical science and technology are defined clearly in concept, the reasoning is rigorous and logical and its mathematics requires its pursuit of precision in the work, and a mathematical language to reveal the internal relations between the present images. Medical technology for tissue damage mild disease is often unchecked, when the patient’s own symptoms and feelings are often more accurate than the instrument. Inspired by this, this paper integrates the deep learning model to construct the intelligent diagnostic system. The gray relation is designed to improve the traditional CNN model and the revised algorithms also combine the sensitive data analysis framework. At the meanwhile, application scenario on the professional sports club social responsibility is demonstrated. Experimental results prove the effectiveness of the designed system. The diagnostic accuracy has reached 98.38% which performs better compared with the other state-of-the-art methodologies.