一种多视角人脸检测方法

目前的人脸检测方法多是针对正面人脸,而对于多视角人脸检测还存在很大困难,有效的方法还不多。本文考虑到人脸检测中旋转人脸和侧面人脸两种多视角情况,提出了一种多视角人脸检测方法。针对平面内旋转的问题,在YCbCr色彩空间内建立肤色模型,经过处理确定人脸椭圆区域,利用基于灰度加权的主成分分析算法进行人脸的角度校正,得到偏转校正后的人脸图像。针对侧面人脸的问题,通过上下和左右2个方向的人脸旋转样本库来训练分类器,然后组合成并联分类器,再对偏转校正后的人脸图像进行人脸验证。实验结果表明,该方法可以对任意视角的人脸进行有效的检测,且有较高的检测率。     

敏感图片自动检测系统的研究

设计并实现了一个敏感图片自动识别系统。该系统首先利用肤色模型从敏感图片中检测出肤色区域,再从图片肤色区域中,提取大量经验特征表示图像内容,然后采用Haar特征算法设计一个检测敏感部位对象的分类器,最后通过敏感部位的几何特征逐级识别正常图片与敏感图片。该检测系统具有良好的系统性能。     

Latch Applicator for Efficient Delivery of Dissolving Microneedles Based on Rapid Release of Elastic Strain Energy by Thumb Force (Adv. Funct. Mater. 11/2023)

Dissolving microneedle (DMN) is an attractive alternative to parenteral and enteral drug administration owing to its painless self-administration and safety due to non-generation of medical waste. For reproducible and efficient DMN administration, various DMN application methods, such as weights, springs, and electromagnetic devices, have been studied. However, these applicators have complex structures that are complicated to use and high production costs. In this study, a latch applicator that consists of only simple plastic parts and operates via thumb force without any external complex device is developed. Protrusion-shaped latches and impact distances are designed to accumulate thumb force energy through elastic deformation and to control impact velocity. The optimized latch applicator with a pressing force of 25 N and an impact velocity of 5.9 m s⁻¹ fully inserts the drug-loaded tip of the two-layered DMN into the skin. In an ovalbumin immunization test, DMN with the latch applicator shows a significantly higher IgG antibody production rate than that of intramuscular injection. The latch applicator, which provides effective DMN insertion and a competitive price compared with conventional syringes, has great potential to improve delivery of drugs, including vaccines.     

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

基于MultiResUNet-SMIS的皮肤黑色素瘤图像分割

针对现有的皮肤黑色素瘤病灶分割精度不高的问题,结合现有卷积神经网络方法提出皮肤黑色素瘤图像分割方法 MultiResUNet-SMIS.首先,依据皮肤黑色素瘤成像特点,引入不同空洞率的空洞卷积替换普通卷积,在参数量相同的前提下扩大感受野,使网络模型能够适用于多尺度病灶分割任务;其次加入空间和通道注意力机制以重新分配特征权重,扩大感兴趣特征影响,抑制无关特征;最后融合Focal loss与Dice loss提出一种新的loss函数FD loss用于计算回归损失,解决前景背景像素不均衡问题,进一步提高网络模型的分割精度.实验结果表明,MultiResUNet-SMIS在ISIC-2018数据集上的Dice指数、IoU指数以及Acc准确率分别达到了89.47%、82.67%、96.13%,与原MultiResUNet以及UNet、UNet++、DeepLab V3+等主流方法相比, MultiResUNet-SMIS在皮肤黑色素瘤图像分割中具有更好的效果.     

Topical delivery of paclitaxel for treatment of skin cancer

Context: Skin cancer represents the most growing types of cancer in human and ultraviolet radiation can be cited as one of the prime factor for its occurrence. Current therapy of skin cancer suffers from numerous side effects; for effective therapy, topical application of formulation of paclitaxel (PTX) can be considered as a novel approach.

Objective: The present study is an attempt to prepare formulation of solid lipid nanoparticles (SLN) of PTX for the effective treatment of various form of skin carcinoma.

Methods: The SLN were prepared by high-speed homogenization and ultrasonication method. The prepared SLN were characterized. The optimized PTX SLN were loaded in carbopol gel. The prepared gels were evaluated for its gelling properties and finally studied for in vivo anti-cancer efficacy and histopathological study.

Results: The particle size distribution was found to be in the range of 78.82–587.8?nm. The product yield (%) was found between 60% and 66% and showed a highest entrapment efficiency of 68.3%. The in vitro release of the drug from SLN dispersion was found to be biphasic with the initial burst effect, followed by slow release. SLN-loaded gel were subjected to permeability study and the results show steady-state flux (J_ss), permeability coefficient (K_p), and enhancement ratio were significantly increased in SLN-loaded gel formulation as compared with PTX-loaded gel. The histopathological study clearly reveals the efficacy of the SLN-F3 3G in the treatment of skin cancer.

Conclusion: The experimental formulations show controlled release of PTX and thus expected to show reduce dose-related side effects.     

Skin detection for single images using dynamic skin color modeling

Up-to-date skin detection techniques use adaptive skin color modeling to overcome the varying skin color problem. Most methods for tracking skin regions in videos utilize the correlation between contiguous frames. This paper proposes a new approach for detecting skin in a single image. This approach uses a local skin model to shift a globally trained skin model to adapt the final skin model to the current image. Experimental results show that the proposed method can achieve better accuracy. Two improvements for speeding up the processing are also discussed.