In this paper, a helicobacter pylori classification method based on deep neural network (Inception v3) was proposed. The purpose of the proposed model is to provide physicians with reference to the diagnosis of Helicobacter pylori infection for increasing the diagnostic efficiency. Data augmentation and transfer learning are exploited for model construction to generate a classification system with high prediction accuracy. To evaluate the performance of the proposed method, many endoscope images are collected for testing. Experimental results show that the proposed method can well determine whether the input image contains Helicobacter pylori or not.

This paper proposes a novel algorithm and architecture for the adaptive feedback cancellation (AFC) based on the pitch and the formant information for hearing aid (HA) applications. The proposed method, named as Pitch based Formant Estimation (PFE-AFC), has significantly low complexity compared to Prediction Error Method AFC (PEM-AFC). The proposed PFE-AFC consists of a forward and a backward path processing. The forward path processing includes a low complexity pitch based formant estimator for decorrelation filter coefficients update and a pitch based voice activity detector for speech detection, which facilitates the feedback cancellation filter in the backward path to reduce feedback component and maintain speech quality. From system point of view, the PFE-AFC has low complexity overhead since it is easy to share computation resource with other components in the HA system, such as noise reduction and auditory compensation. In addition, the PFE-AFC is suitable for hardware implementation owing to its regular structure. Complexity evaluations show that the PFE-AFC has four orders lower complexity than the PEM-AFC. Simulation results show that the PFE-AFC and the PEM-AFC can achieve similar PESQ (perceptual evaluation speech quality) and ASG (added stable gain). Moreover, the proposed PFE-AFC can outperform the conventional AFC.

In the above paper (ibid., vol. 22, no. 8, pp.1248-1259, Aug. 2014), Table IV is incorrect. The correct table is presented here.