Machine learning approaches for lung cancer diagnosis.

The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide us with a huge amount of data, that could not be read easily by physicians and radiologists, with valuable information that could be used in smart ways to discover new knowledge from these vast quantities of data. Therefore, the design of computer-aided diagnostic (CAD) system, that can be approved for use in clinical practice that aid radiologists in diagnosis and detecting potential abnormalities, is of a great importance. This dissertation deals with the development of a CAD system for lung cancer diagnosis, which is the second most common cancer in men after prostate cancer and in women after breast cancer. Moreover, lung cancer is considered the leading cause of cancer death among both genders in USA. Recently, the number of lung cancer patients has increased dramatically worldwide and its early detection doubles a patient’s chance of survival. Histological examination through biopsies is considered the gold standard for final diagnosis of pulmonary nodules. Even though resection of pulmonary nodules is the ideal and most reliable way for diagnosis, there is still a lot of different methods often used just to eliminate the risks associated with the surgical procedure. Lung nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. A pulmonary nodule is the first indication to start diagnosing lung cancer. Lung nodules can be benign (normal subjects) or malignant (cancerous subjects). Large (generally defined as greater than 2 cm in diameter) malignant nodules can be easily detected with traditional CT scanning techniques. However, the diagnostic options for small indeterminate nodules are limited due to problems associated with accessing small tumors. Therefore, additional diagnostic and imaging techniques which depends on the nodules’ shape and appearance are needed. The ultimate goal of this dissertation is to develop a fast noninvasive diagnostic system that can enhance the accuracy measures of early lung cancer diagnosis based on the well-known hypotheses that malignant nodules have different shape and appearance than benign nodules, because of the high growth rate of the malignant nodules. The proposed methodologies introduces new shape and appearance features which can distinguish between benign and malignant nodules. To achieve this goal a CAD system is implemented and validated using different datasets. This CAD system uses two different types of features integrated together to be able to give a full description to the pulmonary nodule. These two types are appearance features and shape features. For the appearance features different texture appearance descriptors are developed, namely the 3D histogram of oriented gradient, 3D spherical sector isosurface histogram of oriented gradient, 3D adjusted local binary pattern, 3D resolved ambiguity local binary pattern, multi-view analytical local binary pattern, and Markov Gibbs random field. Each one of these descriptors gives a good description for the nodule texture and the level of its signal homogeneity which is a distinguishable feature between benign and malignant nodules. For the shape features multi-view peripheral sum curvature scale space, spherical harmonics expansions, and different group of fundamental geometric features are utilized to describe the nodule shape complexity. Finally, the fusion of different combinations of these features, which is based on two stages is introduced. The first stage generates a primary estimation for every descriptor. Followed by the second stage that consists of an autoencoder with a single layer augmented with a softmax classifier to provide us with the ultimate classification of the nodule. These different combinations of descriptors are combined into different frameworks that are evaluated using different datasets. The first dataset is the Lung Image Database Consortium which is a benchmark publicly available dataset for lung nodule detection and diagnosis. The second dataset is our local acquired computed tomography imaging data that has been collected from the University of Louisville hospital and the research protocol was approved by the Institutional Review Board at the University of Louisville (IRB number 10.0642). These frameworks accuracy was about 94%, which make the proposed frameworks demonstrate promise to be valuable tool for the detection of lung cancer

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

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

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%

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

A Novel Autoencoder-Based Diagnostic System for Early Assessment of Lung Cancer

© 2018 IEEE. A novel framework for the classification of lung nodules using computed tomography (CT) scans is proposed in this paper. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following two groups of features: (i) appearance features that is 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) geometric features that describe the shape geometry of the lung nodules. The novelty of this paper 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 geometric features. Finally, a deep autoencoder (AE) classifier is fed by the above two feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium (LIDC). We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 92.20%

Protective Effects of Vasodilatory Î’eta-Blockers Carvedilol and Nebivolol against Glycerol Model of Rhabdomyolysis-Induced Acute Renal Failure in Rats

BACKGROUND: Rhabdomyolysis (RM)-induced acute renal failure (ARF) accounts for about 10–40% of all cases of ARF.AIM: The present study investigated the possible protective effect of two nitric oxides (NO)-releasing third generation β-blockers, carvedilol (Carv) and nebivolol (Nebi), against RM-mimicking glycerol (Gly)-induced ARF in rats.MATERIAL AND METHODS: After 24 h dehydration, rats received a single dose of 50% Gly (8 ml/kg, im). They were treated with vehicle, Carv (2.5 mg/kg/day, po) or Nebi (10 mg/kg, po) for 3 successive days starting from an hour prior to Gly injection. Evaluation of blood pressure and locomotor activity was performed during the experiment. 72 h following Gly administration, total protein in the urine, serum levels of creatinine, blood urea nitrogen, sodium and potassium as well as the renal contents of malondialdehyde, reduced glutathione and NO were assessed, together with a histopathological examination of renal tissues.RESULTS: Carv and Nebi attenuated Gly-induced renal dysfunction and histopathological alterations. They decreased the Gly-induced oxidative stress and increased renal NO concentration. Restoration of normal blood pressure and improvement of locomotor activity were also observed.CONCLUSION: The results clearly demonstrate protective effects of Carv and Nebi against renal damage involved in RM-induced ARF and suggest a role of their antioxidant and NO-releasing properties

A generalized deep learning-based diagnostic system for early diagnosis of various types of pulmonary nodules

© The Author(s) 2018. A novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: (1) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventh-order Markov Gibbs random field 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 geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%