Computer-aided diagnosis in clinical endoscopy using neuro-fuzzy systems

In this paper, an innovative detection system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images is presented. The images used in this study have been obtained using the new M2A Swallowable Imaging Capsule - a patented, video colourimaging disposable capsule. Schemes have been developed to extract new texture features from the texture spectra in the hromatic and achromatic domains for a selected region of nterest from each colour component histogram of endoscopic images. The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and artificial neural networks and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this paper. The detection accuracy of the proposed system has reached to loo%, providing thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in endoscopy

An efficient fuzzy based technique for signal classification

Fuzzy systems are currently finding practical applications, ranging from "soft" regulatory control in consumer products to accurate modelling of non-linear systems. This paper presents the design of a classification system for vehicle acoustic signal classification. Traffic management and information systems rely on a suite of sensors for estimating traffic parameters. Currently inductive loop detectors and video-based systems are often used to count and detect vehicles. Loop detectors are expensive to maintain and video-based systems are sensitive to environmental conditions and do not perform well in vehicle classification. Vehicle classification is important in the computation of the percentages of vehicle classes that use streets and motorways. The use of an automated system can lead to adequate road surface maintenance with obvious results in cost and quality. However the sound of a working vehicle could provide an important clue to the vehicle type. A novel approach, based on adaptive fuzzy logic systems, has been discussed in this paper. Its performance is evaluated through a simulation study, using metered data collected from a roadside microphone-array sensor at the Valle d'Aosta highway in north-western Italy. The results indicate that the fuzzy classifier based on the proposed defuzzification method, namely area of balance (AOB), provide more accurate classifications compared to other classifiers