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One of the most serious and deadly diseases in the world is lung cancer. On the other hand, prompt diagnosis, as well as care, could save lives. Probably the most capable imaging method in the medical world, computed tomography (CT) scans are challenging for clinicians to analyze as well as detect cancer. In recent years, there has been an increase in the use of image analysis techniques for the detection of CT scan images matching cancer tissues. Using a Computer-aided detection (CAD) system employing CT scans to aid inside the early lung cancer diagnosis as well as to differentiate among benign/malignant tumors is thus interesting to address. The primary objective of this study would be to assess several computer-aided approaches, analyze the right methodology already in use, and afterward propose a new approach that integrates enhancements to the best system currently in use. This research improves the performance of the existing retrieval system by combining various image feature extraction processes and modifying the internal layer section of the classifier. The segmentation method proposed here to identify cancer is Improved Random Walker segmentation along with Random Forest (RF) classifier and K-Nearest Neighbors (KNN) classifier. Here, the research is accomplished on the Lung Image database consortium (LIDC) datasets which is a collection of CT images and is utilized as the input images to verify the effectiveness of the suggested strategy. The accuracy of the proposed method for the detection of lung cancer with the aid of the RF classifier is 99.6 % as well as the KNN classifier is 96.4% accordingly.

Volumetric Modulated Arc Therapy (VMAT) is an important modality for radical radiotherapy of all major treatment sites. This study aims to compare Analytical Anisotropic Algorithm (AAA) and the two dose-reporting modes of Acuros XB (AXB) algorithm -the dose to medium option (D

In the present research, we have developed a Unified Dosimetry Quality Audit Index (UDQAI) decision support system supplemented with treatment planning systems for radiation oncologists. This will aid the radiotherapy treatment of Glioblastoma Multiforme and is based on the Integrated Monte Carlo Model (IMC). IMC model is a quality assurance strategy for the computation of total dose, scatter dose deposition at the GBM site and healthy tissues/layers within the brain. It is a combination of Proliferation-Hypoxia-Invasion-Necrosis-Angiogenesis-Radiotherapy-Quality Assurance model, Radiation-induced damage-Quality Assurance model, Boltzmann radiation transport, Talaraich Tournoux image coordinate system for positioning of the tumour from the CT image and GBM treatment environments/patient case reports. The IMC model was validated by recreating GBM patient treatment environments on a novel computational heterogeneous phantom, the Mathematical Anthropomorphic Brain (MAB) phantom. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost effective; nor patient-specific and are non-customised and less accurate. Thirty-Eight patient-specific GBM treatment environments were recreated on the MAB phantom. MAB phantom synthesis requires mimicking real human brain tissues/layers. Open-source protein databases such as UniProt, Swiss-model and Peptide were atlas employed to compute the elemental composition of different brain layers/tissues. Brain layers and tissues were synthesised as slabs using the Constructive Solid Geometry technique within the MAB phantom on the Electron Gamma Shower radiation transport platform. Phantom slab dimensions were computed by superimposing CT scan images of the brain with GBM and associated comorbidities on Talairach-Tournoux coordinate system. Slab surfaces of the phantom were defined by constructive solid geometry approach using quadratic equations. Energy deposition inside different slabs of phantom is calculated by Analog Monte Carlo game. Computed total dose and scatter dose deposition within the tumour and normal tissues/brain layers were graded by UDQAI which ensures planned dose delivery to the tumour site for radiation-induced cancer cell death minimising healthy tissue damages. The results of the present experimentation show that the proposed framework is promising and outperforms other recent deep learning-based decision systems. Deep learning-based decision systems are a hidden process which is unaware of the physical transport process of charged particles. UDQAI classification of treatment environments predicts that 76.32% of the total environs deposited a substantial amount of dose to the GBM locus. During treatment, healthy tissues and brain layers receive a part of transported energy. This fact is reinforced by the average deviation at the GBM site −8.2% on the contrary, healthy tissues encircling GBM reported −3.909%. These encouraging results would pave the way for the development of a biomathematical tumour growth model and Monte Carlo radiation transport-linked decision assist algorithm for radiation oncologists in the near future.