A recent review and a taxonomy for hard and soft tissue visualization-based mixed reality.

Background: Mixed reality (MR) visualization is gaining popularity in image-guided surgery (IGS) systems, especially for hard and soft tissue surgeries. However, a few MR systems are implemented in real time. Some factors are limiting MR technology and creating a difficulty in setting up and evaluating the MR system in real environments.

A novel enhanced softmax loss function for brain tumour detection using deep learning.

Background And Aim: In deep learning, the sigmoid function is unsuccessfully used for the multiclass classification of the brain tumour due to its limit of binary classification. This study aims to increase the classification accuracy by reducing the risk of overfitting problem and supports multi-class classification. The proposed system consists of a convolutional neural network with modified softmax loss function and regularization.

A novel mixed reality in breast and constructive jaw surgical tele-presence.

Background And Aim: Surgical telepresence has been implemented using Mixed reality (MR) but, MR is theory based and only used for investigating research. The Aim of this paper is to propose and implement a new solution by merging augmented video (generating in local site) and virtual expertise surgeon hand (remote site). This system is to improve the visualization of surgical area, overlay accuracy in the merged video without having any discoloured patterns on hand, smudging artefacts on surgeon hand boundary and occluded areas of surgical area.

A novel optimized initial cluster center and enhanced objective function: Medical diagnosis through classification.

Medical diagnosis through classification is often critical as the medical datasets are multilabel in nature, that is, a patient may have more than one health condition: high blood pressure, obesity, and diabetes. The aim of this article is to improve the accuracy and performance of multilabel classification using multilabel feature selection and improved overlapping clustering method. The proposed system consists of Optimized Initial Cluster Centers and Enhanced Objective Function technique to reduce the number of iterations in the clustering process thereby improving the clustering performance and to improve the clustering accuracy which will result in improving the accuracy and performance of multilabel classification.