(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 × 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

In order to detect hearing loss more efficiently and accurately, this study proposed a new method based on fractional Fourier transform (FRFT). Three-dimensional volumetric magnetic resonance images were obtained from 15 patients with left-sided hearing loss (LHL), 20 healthy controls (HC), and 14 patients with right-sided hearing loss (RHL). Twenty-five FRFT spectrums were reduced by principal component analysis with thresholds of 90%, 95%, and 98%, respectively. The classifier is the single-hidden-layer feed-forward neural network (SFN) trained by the Levenberg–Marquardt algorithm. The results showed that the accuracies of all three classes are higher than 95%. In all, our method is promising and may raise interest from other researchers.

Using the 3D printed mold-removal method to fabricate microchannel has become a promising alternative to the conventional soft lithography technique, due to the convenience in printing channel mold and the compatibility with PDMS material. Although having great potential, the use of single filament extruded by fused deposition modeling (FDM) as the sacrificial channel mold has not been elaborately studied. In this paper, we demonstrate the fabrication of microchannels with different structure and size by controllably extruding the sacrificial channel molds. The influences of the main processing parameters including working distance, extrusion amount and printing speed on the printed microchannels are systematically investigated. The results show that, the circular and low-aspect-ratio straight microchannels with different sizes can be fabricated by adjusting the extrusion amounts. The sinusoidal, 3D curved and cross-linked curved microchannels along straight path can be fabricated, either independently or in combination, by the combined control of the working distance, extrusion amount and printing speed. The complex microchannels with different structural features can also be printed along curved serpentine, rectangular serpentine, and spiral paths. This paper presents a simple and powerful method to fabricate the complex microchannels with different structure and size by just controlling the processing parameters for extruding channel molds.

Microfluidic chips have been widely used in many areas such as biology, environmental monitoring, and micromixing. With the increasing popularity and complexity of microfluidic systems, rapid and convenient approaches for fabricating microfluidic chips are necessary. In this study, a method based on EHD (electrohydrodynamic)-assisted direct printing is proposed. Firstly, the principle of EHD-assisted direct printing was analyzed. The influence of the operating voltage and moving speed of the work table on the width of a paraffin wax model was studied. Then, two kinds of paraffin wax molds for micromixing with channel widths of 120 μm were prepared. A polydimethylsiloxane (PDMS) micromixer was fabricated by replicating the paraffin wax mold, and the micromixing of blue and yellow dye was realized. The results show that EHD-assisted direct printing can be used to make complex microscale structures, which has the potential to greatly simplify the manufacturing process.

One of eight women can get breast cancer during all her life. This study used Hu moment invariant and feedforward neural network to diagnose breast cancer. With the help of K-fold cross validation, we can test the out-of-sample accuracy of our method. Finally, we found that our methods can improve the accuracy of detecting breast cancer and reduce the difficulty of judging.