Diabetic wounds and artificial intelligence:A mini-review

Diabetic wound takes longer time to heal due to micro and macro-vascular ailment.This longer healing time can lead to infections and other health complications.Foot ulcers are one of the most common diabetic wounds.These are one of the leading cause of amputations.Medical science is continuously striving for improving quality of human life.A recent trend of amalgamation of knowledge,efforts and technological advancement of medical science experts and artificial intelligence researchers,has made tremendous success in diagnosis,prognosis and treatment of a variety of diseases.Diabetic wounds are no exception,as artificial intelligence experts are putting their research efforts to apply latest technological advancements in the field to help medical care personnel to deal with diabetic wounds in more effective manner.The presented study reviews the diagnostic and treatment research under the umbrella of Artificial Intelligence and computational science,for diabetic wound healing.Framework for diabetic wound assessment using artificial intelligence is presented.Moreover,this review is focused on existing and potential contribution of artificial intelligence to improve medical services for diabetic wound patients.The article also discusses the future directions for the betterment of the field that can lead to facilitate both,clinician and patients.

A Fast Panoptic Segmentation Network for Self-Driving Scene Understanding

In recent years,a gain in popularity and significance of science understanding has been observed due to the high paced progress in computer vision techniques and technologies.The primary focus of computer vision based scene understanding is to label each and every pixel in an image as the category of the object it belongs to.So it is required to combine segmentation and detection in a single framework.Recently many successful computer vision methods has been developed to aid scene understanding for a variety of real world application.Scene understanding systems typically involves detection and segmentation of different natural and manmade things.A lot of research has been performed in recent years,mostly with a focus on things(a well-defined objects that has shape,orientations and size)with a less focus on stuff classes(amorphous regions that are unclear and lack a shape,size or other characteristics Stuff region describes many aspects of scene,like type,situation,environment of scene etc.and hence can be very helpful in scene understanding.Existing methods for scene understanding still have to cover a challenging path to cope up with the challenges of computational time,accuracy and robustness for varying level of scene complexity.A robust scene understanding method has to effectively deal with imbalanced distribution of classes,overlapping objects,fuzzy object boundaries and poorly localized objects.The proposed method presents Panoptic Segmentation on Cityscapes Dataset.Mobilenet-V2 is used as a backbone for feature extraction that is pre-trained on ImageNet.MobileNet-V2 with state-of-art encoder-decoder architecture of DeepLabV3+with some customization and optimization is employed Atrous convolution along with Spatial Pyramid Pooling are also utilized in the proposed method to make it more accurate and robust.Very promising and encouraging results have been achieved that indicates the potential of the proposed method for robust scene understanding in a fast and reliable way.