A Novel Framework for Accurate and Non-Invasive Pulmonary Nodule Diagnosis by Integrating Texture and Contour Descriptors

An accurate computer aided diagnostic (CAD) system is very significant and critical for early detection of lung cancer. A new framework for lung nodule classification is proposed in this paper using different imaging markers from one computed tomography (CT) scan. Texture and shape features are combined together to show the main discriminative characteristics between malignant and benign pulmonary nodules. 7th-Order Markov Gibbs random field, (MGRF), is implemented to give a good description of the nodule’s appearance by involving the spatial data. A Various-views Marginal Aggregation Curvature Scale Space (MACSS) and the primitive geometrical properties are used to indicate the nodule’s shape complexity. Eventually, all these modeled descriptors are combined using a stacked autoencoder and softmax classifier to give the final diagnosis. Our system has been validated using 727 samples from the Lung Image Database Consortium. Our diagnosis framework’s accuracy, sensitivity, and specificity were 94.63%, 93.86%, 94.78% respectively, showing that our system serves as an important clinical assistive tool

Early Diagnosis System For Lung Nodules Based On The Integration Of A Higher-Order Mgrf Appearance Feature Model And 3d-Cnn

In this chapter, a new system for lung nodule diagnosis, using features extracted from one computed tomography (CT) scan, is presented. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following two groups of features: (i) appearance features that are modeled using higher-order Markov–Gibbs random field (MGRF)-model that has the ability to describe the spatial inhomogeneities inside the lung nodule; and (ii) local features that are extracted using 3D convolutional neural networks (3D-CNN) because of its ability to exploit the spatial correlation of input data in an efficient way. The novelty of this chapter is to accurately model the appearance of the detected lung nodules using a new developed 7th-order MGRF model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted local features from 3D-CNN. Finally, a deep autoencoder (AE) classifier is fed by the above two feature groups to distinguish between the malignant and benign nodules

A Comprehensive Framework for Accurate Classification of Pulmonary Nodules

© 2020 IEEE. A precise computerized lung nodule diagnosis framework is very important for helping radiologists to diagnose lung nodules at an early stage. In this manuscript, a novel system for pulmonary nodule diagnosis, utilizing features extracted from single computed tomography (CT) scans, is proposed. This system combines robust descriptors for both texture and contour features to give a prediction of the nodule\u27s growth rate, which is the standard clinical information for pulmonary nodules diagnosis. Spherical Sector Isosurfaces Histogram of Oriented Gradient is developed to describe the nodule\u27s texture, taking spatial information into account. A Multi-views Peripheral Sum Curvature Scale Space is used to demonstrate the nodule\u27s contour complexity. Finally, the two modeled features are augmented together utilizing a deep neural network to diagnose the nodules malignancy. For the validation purpose, the proposed system utilized 727 nodules from the Lung Image Database Consortium. The proposed system classification accuracy was 94.50%

Radiomic-based framework for early diagnosis of lung cancer

© 2019 IEEE. This paper proposes a new framework for pulmonary nodule diagnosis using radiomic features extracted from a single computed tomography (CT) scan. The proposed framework integrates appearance and shape features to get a precise diagnosis for the extracted lung nodules. The appearance features are modeled using 3D Histogram of Oriented Gradient (HOG) and higher-order Markov Gibbs random field (MGRF) model because of their ability to describe the spatial non-uniformity in the texture of the nodule regardless of its size. The shape features are modeled using Spherical Harmonic expansion and some basic geometric features in order to have a full description of the shape complexity of the nodules. Finally, all the modeled features are fused and fed to a stacked autoencoder to differentiate between the malignant and benign nodules. Our framework is evaluated using 727 nodules which are selected from the Lung Image Database Consortium (LIDC) dataset, and achieved classification accuracy, sensitivity, and specificity of 93.12%, 92.47%, and 93.60% respectively

A Novel CT-Based Descriptors for Precise Diagnosis of Pulmonary Nodules

© 2019 IEEE. Early diagnosis of pulmonary nodules is critical for lung cancer clinical management. In this paper, a novel framework for pulmonary nodule diagnosis, using descriptors extracted from single computed tomography (CT) scan, is introduced. This framework combines appearance and shape descriptors to give an indication of the nodule prior growth rate, which is the key point for diagnosis of lung nodules. Resolved Ambiguity Local Binary Pattern and 7th Order Markov Gibbs Random Field are developed to describe the nodule appearance without neglecting spatial information. Spherical harmonics expansion and some primitive geometric features are utilized to describe how the nodule shape is complicated. Ultimately, all descriptors are combined using denoising autoencoder to classify the nodule, whether malignant or benign. Training, testing, and parameter tuning of all framework modules are done using a set of 727 nodules extracted from the Lung Image Database Consortium (LIDC) dataset. The proposed system diagnosis accuracy, sensitivity, and specificity were 94.95%, 94.62%, 95.20% respectively, all of which show that our system has promise to reach the accepted clinical accuracy threshold

A New System for Lung Cancer Diagnosis based on the Integration of Global and Local CT Features

© 2019 IEEE. Lung cancer leads deaths caused by cancer for both men and women worldwide, that is why creating systems for early diagnosis with machine learning algorithms and nominal user intervention is of huge importance. In this manuscript, a new system for lung nodule diagnosis, using features extracted from one computed tomography (CT) scan, is presented. This system integrates global and local features to give an implication of the nodule prior growth rate, which is the main point for diagnosis of pulmonary nodules. 3D adjustable local binary pattern and some basic geometric features are used to extract the nodule global features, and the local features are extracted using 3D convolutional neural networks (3D-CNN) because of its ability to exploit the spatial correlation of input data in an efficient way. Finally all these features are integrated using autoencoder to give a final diagnosis for the lung nodule whether benign or malignant. The system was evaluated using 727 nodules extracted from the Lung Image Database Consortium (LIDC) dataset. The proposed system diagnosis accuracy, sensitivity, and specificity were 92.20%,93.55%, and 91.20% respectively. The proposed framework demonstrated its promise as a valuable tool for lung cancer detection evidenced by its higher accuracy

Adsorptive Membranes Incorporating Ionic Liquids (ILs), Deep Eutectic Solvents (DESs) or Graphene Oxide (GO) for Metal Salts Extraction from Aqueous Feed

Water scarcity is a significant concern, particularly in arid regions, due to the rapid growth in population, industrialization, and climate change. Seawater desalination has emerged as a conventional and reliable solution for obtaining potable water. However, conventional membrane-based seawater desalination has drawbacks, such as high energy consumption resulting from a high-pressure requirement, as well as operational challenges like membrane fouling and high costs. To overcome these limitations, it is crucial to enhance the performance of membranes by increasing their efficiency, selectivity, and reducing energy consumption and footprint. Adsorptive membranes, which integrate adsorption and membrane technologies, offer a promising approach to address the drawbacks of standalone membranes. By incorporating specific materials into the membrane matrix, composite membranes have demonstrated improved permeability, selectivity, and reduced pressure requirements, all while maintaining effective pollutant rejection. Researchers have explored different adsorbents, including emerging materials such as ionic liquids (ILs), deep eutectic solvents (DESs), and graphene oxide (GO), for embedding into membranes and utilizing them in various applications. This paper aims to discuss the existing challenges in the desalination process and focus on how these materials can help overcome these challenges. It will also provide a comprehensive review of studies that have reported the successful incorporation of ILs, DESs, and GO into membranes to fabricate adsorptive membranes for desalination. Additionally, the paper will highlight both the current and anticipated challenges in this field, as well as present prospects, and provide recommendations for further advancements

Investigating the Potential Use of Ionic Liquids in Pre-Treatment Application for Water Desalination

The world has been noticing a quickening advancement over the last decades in terms of irrigated agriculture, social, industrial, and economical perspectives; followed by a huge increment in the water demand. Therefore, desalination is used all over the world to reduce worldwide water shortage; however, the traditional techniques lead to fossil fuel depletion and global warming. Therefore, scientists are investigating new green and environment-friendly methods to be used by the desalination plants to reduce CO2 emissions and save the natural resources. In this study, the feasibility of using ionic liquids (ILs) as potential means for pre-treatment of seawater desalination was examined. The main aim of this work was to examine the ability of ILs in extracting salts from saline water. As a first step, the solubility of NaCl, MgCl2, and CaCl2 in different ILs at different temperatures were analyzed. The solubility of the salts in ILs increased in most cases with temperature increments; however, the presence of emulsion was seen in a few cases. The highest measured concentration of NaCl was 6,639 ppm at 60 °C in 1,3-Dimethylimidazolium dimethyl phosphate

On the Integration of CT-Derived Features for Accurate Detection of Lung Cancer

© 2018 IEEE. Lung cancer is one of the unsafe maladies that reason enormous disease passing around the world. Early and accurate detection of lung cancer is the main conceivable approach to enhance patients\u27 survival rate. In this paper, we proposes a new framework for pulmonary nodule diagnosis using various features extracted from a single computed tomography (CT) scan. The proposed system fuse texture and shape features to get an accurate diagnosis for the extracted lung nodules. 3D Local Binary Pattern (LBP) and higher-order Markov Gibbs random field (MGRF) models are utilized to model the texture appearance due to their capability to give a precise description for the spatial non-uniformity in the texture of the nodules. Spherical Harmonic expansion and some basic geometric features are utilized to model the shape features due to their capability to give a full description of the shape complexity of the nodules. Finally, all the modeled features are fused and fed to a stacked autoencoder to differentiate between the malignant and benign nodules. Our framework is evaluated using 727 nodules which are selected from the Lung Image Database Consortium (LIDC) dataset, and achieved classification accuracy, sensitivity, and specificity of 92.66%, 95.70%, and 90.40% respectively

A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work