Scalable Skin Lesion Multi-Classification Recognition System

Skin lesion recognition is an important challenge in the medical field.In this paper,we have implemented an intelligent classification system based on convolutional neural network.First of all,this system can classify whether the input image is a dermascopic image with an accuracy of 99%.And then diagnose the dermoscopic image and the non-skin mirror image separately.Due to the limitation of the data,we can only realize the recognition of vitiligo by non-skin mirror.We propose a vitiligo recognition based on the probability average of three structurally identical CNN models.The method is more efficient and robust than the traditional RGB color space-based image recognition method.For the dermoscopic classification model,we were able to classify 7 skin lesions,use weighted optimization to overcome the unbalanced data set,and greatly improve the sensitivity of the model by means of model fusion.The optimization and expansion of the system depend on the increase of database.

Classification and Research of Skin Lesions Based on Machine Learning

Classification of skin lesions is a complex identification challenge.Due to the wide variety of skin lesions,doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy.The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention.With the development of deep learning,the field of image recognition has made long-term progress.The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology.In this work,we try to classify seven kinds of lesion images by various models and methods of deep learning,common models of convolutional neural network in the field of image classification include ResNet,DenseNet and SENet,etc.We use a fine-tuning model with a multi-layer perceptron,by training the skin lesion model,in the validation set and test set we use data expansion based on multiple cropping,and use five models’ensemble as the final results.The experimental results show that the program has good results in improving the sensitivity of skin lesion diagnosis.

3D Printed Bioinspired Stents with Photothermal Effects for Malignant Colorectal Obstruction

Stent placement is an effective palliation therapy for malignant colorectal obstruction.However,recurrent obstruction is a common severe complication caused by tumor ingrowth into the stent lumen.Conventional covered stents play a part in preventing the tumor from growing inward but at the expense of significantly increasing the risk of stent migration.Therefore,there is an urgent demand to develop stents with sustained antitumor and antimigration abilities.Herein,we propose a facile method for fabricating multifunctional bioinspired colorectal stents using 3D printing technology.Inspired by high-adhesion biological structures(gecko feet,tree frog toe pads,and octopus suckers)in nature,different types of bioinspired colorectal stents are designed to reduce migration.After functionalization with graphene oxide(GO),bioinspired colorectal stents show excellent and controllable photothermal performance,which is validated by effective ablation of colon cancer cells in vitro and tumors in vivo.Besides,the bioinspired colorectal stents demonstrate the feasibility of transanal placement and opening of the obstructed colon.More importantly,the facile manufacturing process of multifunctional bioinspired colorectal stents is appealing for mass production,Hence,the developed multifunctional bioinspired colorectal stents exhibit a highly promising potential in clinical applications.