Enhanced lung cancer detection: Integrating improved random walker segmentation with artificial neural network and random forest classifier.

Background: Medical image segmentation is a vital yet difficult job because of the multimodality of the acquired images. It is difficult to locate the polluted area before it spreads. Methods: This research makes use of several machine learning tools, including an artificial neural network as well as a random forest classifier, to increase the system's reliability of pulmonary nodule classification. Anisotropic diffusion filtering is initially used to remove noise from a picture. After that, a modified random walk method is used to get the region of interest inside the lung parenchyma. Finally, the features corresponding to the consistency of the picture segments are extracted using texture-based feature extraction for pulmonary nodules. The final stage is to identify and classify the pulmonary nodules using a classifier algorithm. Results: The studies employ cross-validation to demonstrate the validity of the diagnosis framework. In this instance, the proposed method is tested using CT scan information provided by the Lung Image Database Consortium. A random forest classifier showed 99.6 percent accuracy rate for detecting lung cancer, compared to a artificial neural network's 94.8 percent accuracy rate. Conclusions: Due to this, current research is now primarily concerned with identifying lung nodules and classifying them as benign or malignant. The diagnostic potential of machine learning as well as image processing approaches are enormous for the categorization of lung cancer.

A Dosimetric Study Comparing Different Radiotherapy Planning Techniques With and Without Deep Inspiratory Breath Hold for Breast Cancer.

Objective: To analyze whether deep inspiratory breath hold (DIBH) would be dosimetrically beneficial irrespective of radiotherapy planning techniques for patients with left breast cancers requiring adjuvant radiotherapy. Methods: Planning CT scans were taken in free-breathing (FB) as well as deep-inspiration breath hold (DIBH) for patients requiring adjuvant radiotherapy for left breast cancers. After registration, three radiotherapy plans - 3D-conformal radiotherapy (3DCRT), intensity modulated RT (IMRT), and volumetric modulated arc-therapy (VMAT) - were generated for both FB and DIBH scans for each patient. The dose-volume parameters were collected from the dose-volume histogram and analyzed. A paired t-test is used for statistical analysis of the parameters. Findings: The study was conducted on thirteen patients. The mean dose of the left lung was reduced with DIBH by 32%, 24%, and 6% (8.6 Gy, 6.6 Gy, and 6.4 Gy) with 3DCRT, IMRT, and VMAT, respectively. The mean heart dose was reduced by 3.3 Gy (2.2 vs 5.5 Gy), 2.2 Gy (7.5 vs 9.7 Gy), and 1.2 Gy (5.8 vs 7 Gy) with 3DCRT, IMRT, and VMAT with DIBH. Similarly, the left anterior descending artery (LAD) mean dose was relatively reduced by 80%, 34%, and 20% when compared with the FB scans for 3DCRT, IMRT, and VMAT respectively, with max dose in the 3DCRT plan. Novelty/Applications: DIBH appears to have maximum benefit in achieving a better sparing of organs-at-risk for patients being considered for 3DCRT, and to a lesser extent with even IMRT and VMAT techniques.