Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels

Development and application in clinical practice of Computer-aided Diagnosis systems for the early detection of lung cancer

Lung cancer is the main cause of cancer-related deaths both in Europe and United States, because often it is diagnosed at late stages of the disease, when the survival rate is very low if compared to first asymptomatic stage. Lung cancer screening using annual low-dose Computed Tomography (CT) reduces lung cancer 5-year mortality by about 20% in comparison to annual screening with chest radiography. However, the detection of pulmonary nodules in low-dose chest CT scans is a very difficult task for radiologists, because of the large number (300/500) of slices to be analyzed. In order to support radiologists, researchers have developed Computer aided Detection (CAD) algorithms for the automated detection of pulmonary nodules in chest CT scans. Despite proved benefits of those systems on the radiologists detection sensitivity, the usage of CADs in clinical practice has not spread yet. The main objective of this thesis is to investigate and tackle the issues underlying this inconsistency. In particular, in Chapter 2 we introduce M5L, a fully automated Web and Cloud-based CAD for the automated detection of pulmonary nodules in chest CT scans. This system introduces a new paradigm in clinical practice, by making available CAD systems without requiring to radiologists any additional software and hardware installation. The proposed solution provides an innovative cost-effective approach for clinical structures. In Chapter 3 we present our international challenge aiming at a large-scale validation of state-of-the-art CAD systems. We also investigate and prove how the combination of different CAD systems reaches performances much higher than any best stand-alone system developed so far. Our results open the possibility to introduce in clinical practice very high-performing CAD systems, which miss a tiny fraction of clinically relevant nodules. Finally, we tested the performance of M5L on clinical data-sets. In chapter 4 we present the results of its clinical validation, which prove the positive impact of CAD as second reader in the diagnosis of pulmonary metastases on oncological patients with extra-thoracic cancers. The proposed approaches have the potential to exploit at best the features of different algorithms, developed independently, for any possible clinical application, setting a collaborative environment for algorithm comparison, combination, clinical validation and, if all of the above were successful, clinical practice

Automated Lung Disease Detection and Classification Using Quantum Glowworm Swarm Optimizer with Quasi Recurrent Neural Network on Chest X-Ray Images

Lung diseases or otherwise called respiratory diseases are airborne diseases that affect the lungs and the other tissues of the lungs. Tuberculosis, Coronavirus Disease 2019 (COVID-19), and Pneumonia are a few instances of lung diseases. If the lung disease is diagnosed and treated in the initial stage, the chances of recovery rate and long-term survival rates can be increased. Usually, lung disease is identified by Chest X-Ray (CXR) image examination, skin test, sputum sample test, Computed Tomography (CT) scan examination, and blood test. Because of its non-invasive and convenient evaluation for overall outcomes of the chest situation, Lung disease can be detected by specialized radiologists on CXR images. In recent times, Deep Learning (DL) applies to medical images for disease detection and has proved an effective technique for detecting disease. The recent advancement of DL supports the detection and classification of lung diseases in medicinal imaging. This article presents an Automated Lung Disease Detection Using Quantum Glowworm Swarm Optimization with Quasi Recurrent Neural Network (QGSO-QRNN) model on CXR imaging. The presented QGSO-QRNN technique focuses on the identification of lung diseases using DL concepts. To accomplish this, the presented QGSO-QRNN technique initially performs image pre-processing by the use of the Gaussian Filtering (GF) technique. Besides, the Faster SqueezeNet approach is exploited for feature vector generation. Finally, the QRNN model is applied for precise classification of lung diseases with the QGSO technique as a hyperparameter optimizer. The investigational assessment of the QGSO-QRNN technique is examined by employing standard medical datasets and the outputs display the promising performance of the QGSO-QRNN technique over other existing techniques by means of diverse measures

Lung Cancer Detection through TYDWT Algorithm

Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early detection of lung cancer plays a critical role in its treatment and survival rates. In recent years, computer-aided diagnosis systems have been developed to assist radiologists in detecting lung nodules in computed tomography (CT) images. This paper proposes a novel approach for lung cancer detection using Transverse Dyadic Wavelet Transform (TDWT) for feature extraction and classification. TDWT is a multi-resolution analysis technique that can capture both time and frequency information of the input images. The TYDWT algorithm is applied to the lung CT scan images to decompose the images into different sub-bands at multiple scales. The extracted features from these sub-bands are then used to train a machine learning model for lung cancer detection. The performance of the proposed method is evaluated on a publicly available dataset, achieving an accuracy of 95.6% and a sensitivity of 95.2%. The proposed method shows promising results for automated lung cancer detection, which can improve the accuracy and efficiency of the diagnosis process. The results demonstrate that the proposed approach using TDWT can be an effective method for early detection of lung cancer

Deep Learning Models For Biomedical Data Analysis

The field of biomedical data analysis is a vibrant area of research dedicated to extracting valuable insights from a wide range of biomedical data sources, including biomedical images and genomics data. The emergence of deep learning, an artificial intelligence approach, presents significant prospects for enhancing biomedical data analysis and knowledge discovery. This dissertation focused on exploring innovative deep-learning methods for biomedical image processing and gene data analysis. During the COVID-19 pandemic, biomedical imaging data, including CT scans and chest x-rays, played a pivotal role in identifying COVID-19 cases by categorizing patient chest x-ray outcomes as COVID-19-positive or negative. While supervised deep learning methods have effectively recognized COVID-19 patterns in chest x-ray datasets, the availability of annotated training data remains limited. To address this challenge, the thesis introduced a semi-supervised deep learning model named ssResNet, built upon the Residual Neural Network (ResNet) architecture. The model combines supervised and unsupervised paths, incorporating a weighted supervised loss function to manage data imbalance. The strategies to diminish prediction uncertainty in deep learning models for critical applications like medical image processing is explore. It achieves this through an ensemble deep learning model, integrating bagging deep learning and model calibration techniques. This ensemble model not only boosts biomedical image segmentation accuracy but also reduces prediction uncertainty, as validated on a comprehensive chest x-ray image segmentation dataset. Furthermore, the thesis introduced an ensemble model integrating Proformer and ensemble learning methodologies. This model constructs multiple independent Proformers for predicting gene expression, their predictions are combined through weighted averaging to generate final predictions. Experimental outcomes underscore the efficacy of this ensemble model in enhancing prediction performance across various metrics. In conclusion, this dissertation advances biomedical data analysis by harnessing the potential of deep learning techniques. It devises innovative approaches for processing biomedical images and gene data. By leveraging deep learning\u27s capabilities, this work paves the way for further progress in biomedical data analytics and its applications within clinical contexts. Index Terms- biomedical data analysis, COVID-19, deep learning, ensemble learning, gene data analytics, medical image segmentation, prediction uncertainty, Proformer, Residual Neural Network (ResNet), semi-supervised learning

Smart COVID-3D-SCNN: A novel method to classify x-ray images of COVID-19

The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. For extracting the discriminative features through these modalities, deep convolutional neural networks (CNNs) are used. A siamese convolutional neural network model (COVID-3D-SCNN) is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans. To extract the useful features, we used three consecutive models working in parallel in the proposed approach. We acquired 575 COVID-19, 1200 non-COVID, and 1400 pneumonia images, which are publicly available. In our framework, augmentation is used to enlarge the dataset. The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%, specificity 95.55%, and sensitivity 96.62% over (COVID-19 vs. non-COVID19 vs. Pneumonia)

Computed-Tomography (CT) Scan

A computed tomography (CT) scan uses X-rays and a computer to create detailed images of the inside of the body. CT scanners measure, versus different angles, X-ray attenuations when passing through different tissues inside the body through rotation of both X-ray tube and a row of X-ray detectors placed in the gantry. These measurements are then processed using computer algorithms to reconstruct tomographic (cross-sectional) images. CT can produce detailed images of many structures inside the body, including the internal organs, blood vessels, and bones. This book presents a comprehensive overview of CT scanning. Chapters address such topics as instrumental basics, CT imaging in coronavirus, radiation and risk assessment in chest imaging, positron emission tomography (PET), and feature extraction