Lung Cancer Detection from CT Images: Modified Adaptive Threshold Segmentation with Support Vector Machines and Artificial Neural Network Classifier.

Objective: The objective of the research is to implement an advanced modified threshold segmentation and classification model for early and accurate detection of lung cancer from CT images.

Methods: Using the Support Vector Machines (SVM) classifier as well as the Artificial Neural Network (ANN) classifier, the authors propose using Modified adaptive threshold segmentation as a segmentation approach for cancer detection. Here, Lung Image Database Consortium (LIDC) datasets, a collection of CT scans, are used as the video frames in an investigation to authorize the recitation of the suggested technique.

Monte Carlo modelling and validation of the elekta synergy medical linear accelerator equipped with radiosurgical cones.

Monte Carlo simulations of medical linear accelerator heads help in visualizing the energy spectrum and angular spread of photons and electrons, energy deposition, and scattering from each of the head components. Hence, the purpose of this study was to validate the Monte Carlo model of the Elekta synergy medical linear accelerator equipped with stereotactic radio surgical connical collimators. For this, the Elekta synergy medical linear accelerator was modelled using the EGSnrc Monte Carlo code.

Monte Carlo calculated detector-specific correction factors for Elekta radiosurgery cones.

A radiation field is considered small if its dimension is lower than the range of secondary electrons and the collimating devices partially occlude the source. Different detector types, such as unshielded diodes, diamond detectors, and small-volume ion chambers, are used for small-field measurements. Although the active volumes of these detectors are small, their non-water equivalent materials cause response variations.