Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73 %, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.
One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cope with this problem, we propose a novel data augmentation approach based on semi-supervised conditional Generative Adversarial Networks (scGANs), which aims to automatically learn a mapping strategy from a random noise space to original data distribution. The proposed approach has the capability of well synthesizing 'realistic' high-dimensional data, while requiring no additional annotation process. To handle the mode collapse problem of GANs, we further introduce an ensemble strategy to enhance the diversity of the generated data. The systematic experiments conducted on a widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based systems can remarkably outperform other classic data augmentation systems, and are also competitive to other recently reported systems for ASSC.
Three chiral cationic gelators were synthesized. They can form translucent hydrogels in pure water. These hydrogels become highly viscous liquids under strong stirring. Mesoporous silica nanotubes with coiled pore channels in the walls were prepared using the self-assemblies of these gelators as templates. The mechanism of the formation of this hierarchical nanostructure was studied using transmission electron microscopy at different reaction times. The results indicated that there are some interactions between the silica source and the gelator. The morphologies of the self-assemblies of gelators changed gradually during the sol-gel transcription process. It seems that the silica source directed the organic self-assemblies into helical nanostructures.
Single-handed twisted 4,4'-biphenylene-bridged polybissilsesquioxane tubular nanoribbons and single-layered nanoribbons were prepared by tuning the water/ethanol volume ratio in the reaction mixture at pH = 11.6 through a supramolecular templating approach. The single-layered nanoribbons were formed by shrinking tubular nanoribbons after the removal of the templates. In addition, solvent-induced handedness inversion was achieved. The handedness of the polybissilsesquioxanes could be controlled by changing the ethanol/water volume ratio in the reaction mixture. After carbonization at 900 °C for 4.0 h and removal of silica, single-handed twisted carbonaceous tubular nanoribbons and single-layered nanoribbons with micropores in the walls were obtained. X-ray diffraction and Raman spectroscopy analyses indicated that the carbon is predominantly amorphous. The circular dichroism spectra show that the twisted tubular nanoribbons exhibit optical activity, while the twisted single-layered nanoribbons do not. The results shown here indicate that chirality is transferred from the organic self-assemblies to the inner surfaces of the 4,4'-biphenylene-bridged polybissilsesquioxane tubular nanoribbons and subsequently to those of the carbonaceous tubular nanoribbons.
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