Proposed Computer-aided Diagnostic System Research Articles

A Novel System for Precise Grading of Glioma.

Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Accurate grading of gliomas is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive non-invasive multimodal magnetic resonance (MR)-based computer-aided diagnostic (CAD) system to precisely differentiate between different grades of gliomas (Grades: I, II, III, and IV). A total of 99 patients with gliomas (M = 49, F = 50, age range = 1–79 years) were included after providing their informed consent to participate in this study. The proposed imaging-based glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), and gray-level co-occurrence matrix (GLCM), (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and contrast-enhancement slope. These features are then integrated together and processed using a Gini impurity-based selection approach to find the optimal set of significant features. The reduced significant features are then fed to a multi-layer perceptron artificial neural networks (MLP-ANN) classification model to obtain the final diagnosis of a glioma tumor as Grade I, II, III, or IV. The GG-CAD system was evaluated on the enrolled 99 gliomas (Grade I = 13, Grade II = 22, Grade III = 22, and Grade IV = 42) using a leave-one-subject-out (LOSO) and k-fold stratified (with k = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen’s kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at k = 10 and 5. Alternative classifiers, including RFs and SVM produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma.

Hybrid CNN Based Computer-Aided Diagnosis System for Choroidal Neovascularization, Diabetic Macular Edema, Drusen Disease Detection from OCT Images

In the treatment of eye diseases, optical coherence tomography (OCT) is a medical imaging method that displays biological tissue layers by taking high resolution tomographic sections at the micron level. It has an important role in the diagnosis and follow-up of many diseases such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), age-related macular degeneration (AMD), Diabetic Retinopathy, Central Serous Retinopathy, Epiretinal Membrane, and Macular Hole. Computer-Aided Diagnostic (CAD) tools are needed in early detection and treatment monitoring of such eye diseases. In this paper, a hybrid Convolutional Neural Networks-based CAD system, which can classify Diabetic Macular Edema (DME), Drusen Choroidal Neovascularization (CNV), and normal OCT images, is proposed. The proposed system is CNN-SVM (Convolutional Neural Networks – Support Vector Machine) model and doesn’t require any additional extraction of feature or noise filtering on OCT images. A total of 968 OCT images is classified in pre-trained CNN methods with Alexnet, Resnet18 and Googlenet. Accuracy is achieved with highest Googlenet 97.4%. To examine the performance of the proposed CAD system, the CNN-SVM method achieves 98.96% with the highest accuracy hybrid Alexnet-SVM model, which is implemented with Alexnet-SVM, Resnet18-SVM and Googlenet-SVM models.

A novel computer-aided diagnostic system for accurate detection and grading of liver tumors

Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F_{1} score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of 88%pm 5%, 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.

Deep feature extraction and classification of breast ultrasound images

Controlled despeckling (structure/edges/feature preservation with smoothing the homogeneous areas) is a desired pre-processing step for the design of computer-aided diagnostic (CAD) systems using ultrasound images as the presence of speckle noise masks diagnostically important information making interpretation difficult even for experienced radiologist. For efficiently classifying the breast tumors, the conventional CAD system designs use hand-crafted features. However, these features are not robust to the variations in size, shape and orientation of the tumors resulting in lower sensitivity. Thus deep feature extraction and classification of breast ultrasound images have recently gained attention from research community. The deep networks come with an advantage of directly learning the representative features from the images. However, these networks are difficult to train from scratch if the representative training data is small in size. Therefore transfer learning approach for deep feature extraction and classification of medical images has been widely used. In the present work the performance of four pre-trained convolutional neural networks VGG-19, SqueezeNet, ResNet-18 and GoogLeNet has been evaluated for differentiating between benign and malignant tumor types. From the results of the experiments, it is noted that CAD system design using GoogLeNet architecture for deep feature extraction followed by correlation based feature selection and fuzzy feature selection using ANFC-LH yields highest accuracy of 98.0% with individual class accuracy value of 100% and 96% for benign and malignant classes respectively. For differentiating between the breast tumors, the proposed CAD system design can be utilized in routine clinical environment.

A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.

Accurate Classification of Seizure and Seizure-Free Intervals of Intracranial EEG Signals From Epileptic Patients

Electroencephalogram (EEG) signals are widely used to detect epileptic seizures in a patient’s neuronal activity. Since visual inspection and interpretation of EEG signal are time-consuming and prone to errors, various computer-aided diagnostic (CAD) tools have been proposed. In this paper, we present a novel automated detection system to distinguish between intracranial EEG time courses with seizures and those that are seizure-free based on complexity measures. Specifically, the features used to characterize the EEG signals are estimates of multiscaling properties over a large spectrum measured by using the generalized Hurst exponent. We tested the capacity of these estimates to correctly classify seizure intervals using a publicly available data set. Using the k-nearest neighbor classifier and testing with tenfold cross validation, we achieved 100% accurate classification. Our proposed CAD system outperformed the existing state-of-the-art models. Moreover, our CAD system is not only accurate but also fast and simple to implement. Therefore, it can be used as an expert system to support a decision in clinical applications.

Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features.

This work proposes a new reliable computer-aided diagnostic (CAD) system for the diagnosis of breast cancer from breast ultrasound (BUS) images. The system can be useful to reduce the number of biopsies and pathological tests, which are invasive, costly, and often unnecessary. The proposed CAD system classifies breast tumors into benign and malignant classes using morphological and textural features extracted from breast ultrasound (BUS) images. The images are first preprocessed to enhance the edges and filter the speckles. The tumor is then segmented semiautomatically using the watershed method. Having the tumor contour, a set of 855 features including 21 shape-based, 810 contour-based, and 24 textural features are extracted from each tumor. Then, a Bayesian Automatic Relevance Detection (ARD) mechanism is used for computing the discrimination power of different features and dimensionality reduction. Finally, a logistic regression classifier computed the posterior probabilities of malignant vs benign tumors using the reduced set of features. A dataset of 104 BUS images of breast tumors, including 72 benign and 32 malignant tumors, was used for evaluation using an eightfold cross-validation. The algorithm outperformed six state-of-the-art methods for BUS image classification with large margins by achieving 97.12% accuracy, 93.75% sensitivity, and 98.61% specificity rates. Using ARD, the proposed CAD system selects five new features for breast tumor classification and outperforms state-of-the-art, making a reliable and complementary tool to help clinicians diagnose breast cancer.

Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution

For detection and classification of pulmonary nodules, there are two major issues exists in the existing computer aided diagnosis system. First major problem is automatic threshold to segment lungs and nodules. Threshold selection is a critical preprocessing step for medical images. Gaussian approximation based differential evolution has been used to find out the optimal threshold value for segmentation of lungs. Initially, 1-D histogram of the image is estimated using a blend of Gaussian functions whose parameters are calculated using the differential evolution method. Every Gaussian function estimating the histogram characterizes a pixel class and hence a threshold point. Second major problem is to extract the optimized features for classification of nodules. So, a novel gradient intensity feature descriptor for pulmonary nodule classification has been proposed using the multi-coordinate histogram of gradient and intensity based statistical features descriptor. Ensemble bagging trees has been used intelligently using the concepts of ensemble to classify the nodules. We have used standard dataset titled lung image consortium database for the verification and authentication of our proposed computer aided diagnostic (CAD) system. The proposed CAD system gives better results in comparison with existing CAD systems. The sensitivity of 97.5% is attained with an accuracy of 98.7%.

Texture based classification of the severity of mitral regurgitation

Clinically, the severity of valvular regurgitation is assessed by manual tracing of the regurgitant jet in the respective chambers. This work presents a computer-aided diagnostic (CAD) system for the assessment of the severity of mitral regurgitation (MR) based on image processing that does not require the intervention of the radiologist or clinician. Eight different texture feature sets from the regurgitant area (selected through an arbitrary criterion) have been used in the present approach. First order statistics have been used initially, however, observing their limitations, the other texture features such as spatial gray level difference matrix, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws’ textures energy measure, fractal dimension texture analysis and Fourier power spectrum have additionally been used. For the classification task a supervised classifier i.e., support vector machine has been used in the present approach. The classification accuracy has been improved significantly by using these texture features in combination, in comparison to when fed individually as input to the classifier. The classification accuracy of 95.65±1.09, 95.65±1.09 and 95.36±1.13 has been obtained in apical two chamber, apical four chamber and parasternal long axis views, respectively. Therefore, the results of this paper indicate that the proposed CAD system may effectively assist the radiologists in establishing (confirming) the MR stages, namely, mild, moderate and severe.

Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques

A computer-aided diagnostic (CAD) system for effective and accurate pulmonary nodule detection is required to detect the nodules at early stage. This paper proposed a novel technique to detect and classify pulmonary nodules based on statistical features for intensity values using support vector machine (SVM). The significance of the proposed technique is, it uses the nodules features in 2D & 3D and also SVM for the classification that is good to classify the nodules extracted from the image. The lung volume is extracted from Lung CT using thresholding, background removal, hole-filling and contour correction of lung lobe. The candidate nodules are extracted and pruned using the rules based on ground truth of nodules. The statistical features for intensity values are extracted from candidate nodules. The nodule data are up-samples to reduce the biasness. The classifier SVM is trained using data samples. The efficiency of proposed CAD system is tested and evaluated using Lung Image Consortium Database (LIDC) that is standard data-set used in CAD Systems for Lungs Nodule classification. The results obtained from proposed CAD system are good as compare to previous CAD systems. The sensitivity of 96.31% is achieved in the proposed CAD system.

Artificial Neural Network based Classification of Lungs Nodule using Hybrid Features from Computerized Tomographic Images

An automated pulmonary nodule detection system is necessary to help radiologist to identify and detect the nodules at early stage. In this paper, a novel pulmonary nodule detection system is proposed using Artificial Neural Networks (ANN) based on hybrid features consist of 2D and 3D Geometric and Intensity based statistical features. The lung volume is segmented using thresholding, 3D connected component labeling, contour correction and morphological operators. The candidate nodules are extracted and pruned based on the rules that are built using characteristics of nodules. The 2D and 3D Geometric features and Intensity Based Statistical features are extracted and used to train a Neural Network. The proposed Computer-Aided Diagnostic (CAD) system is tested and validated using standard dataset of Lung Image Consortium Database (LIDC). The results obtained from proposed CAD system are good as compared to existing CAD systems. The sensitivity of 96.95% is achieved with accuracy of 96.68%.

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features.

Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

Computer-aided diagnosis in hysteroscopic imaging.

The paper presents the development of a computer-aided diagnostic (CAD) system for the early detection of endometrial cancer. The proposed CAD system supports reproducibility through texture feature standardization, standardized multifeature selection, and provides physicians with comparative distributions of the extracted texture features. The CAD system was validated using 516 regions of interest (ROIs) extracted from 52 subjects. The ROIs were equally distributed among normal and abnormal cases. To support reproducibility, the RGB images were first gamma corrected and then converted into HSV and YCrCb. From each channel of the gamma-corrected YCrCb, HSV, and RGB color systems, we extracted the following texture features: 1) statistical features (SFs), 2) spatial gray-level dependence matrices (SGLDM), and 3) gray-level difference statistics (GLDS). The texture features were then used as inputs with support vector machines (SVMs) and the probabilistic neural network (PNN) classifiers. After accounting for multiple comparisons, texture features extracted from abnormal ROIs were found to be significantly different than texture features extracted from normal ROIs. Compared to texture features extracted from normal ROIs, abnormal ROIs were characterized by lower image intensity, while variance, entropy, and contrast gave higher values. In terms of ROI classification, the best results were achieved by using SF and GLDS features with an SVM classifier. For this combination, the proposed CAD system achieved an 81% correct classification rate.

Design of an Enhanced Fuzzy k-nearest Neighbor Classifier Based Computer Aided Diagnostic System for Thyroid Disease

In this paper, we present an enhanced fuzzy k-nearest neighbor (FKNN) classifier based computer aided diagnostic (CAD) system for thyroid disease. The neighborhood size k and the fuzzy strength parameter m in FKNN classifier are adaptively specified by the particle swarm optimization (PSO) approach. The adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. In addition, we have validated the effectiveness of the principle component analysis (PCA) in constructing a more discriminative subspace for classification. The effectiveness of the resultant CAD system, termed as PCA-PSO-FKNN, has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far via 10-fold cross-validation (CV) analysis, with the mean accuracy of 98.82% and with the maximum accuracy of 99.09%. Promisingly, the proposed CAD system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.

Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems

We present a computer-aided diagnostic (CAD) system with textural features and image retrieval strategies for classifying benign and malignant breast tumors on various ultrasonic systems. Effective applications of CAD have used different types of texture analysis. Nevertheless, most approaches performed in a specific ultrasonic machine do not indicate whether the technique functions satisfactorily for other ultrasonic systems. This study evaluated a series of pathologically proven breast tumors using various ultrasonic systems. Altogether, 600 ultrasound images of solid breast nodules comprising 230 malignant and 370 benign tumors were investigated. All ultrasound images were acquired from four diverse ultrasonic systems. The suspicious tumor area in the ultrasound image was manually chosen as the region-of-interest (ROI) subimage. Textural features extracted from the ROI subimage are supported in classifying the breast tumor as benign or malignant. However, the textural feature always behaves as a high-dimensional vector. In practice, high-dimensional vectors are unsatisfactory at differentiating breast tumors. This study applied the principal component analysis (PCA) to project the original textural features into a lower dimensional principal vector that summarized the original textural information. The image retrieval techniques were employed to differentiate breast tumors, according to the similarities of the principal vectors. The query ROI subimages were identified as malignant or benign tumors according to characteristics of retrieved images from the ultrasound image database. Using the proposed CAD system, historical cases could be directly added into the database without a retraining program. The area under the receiver-operating characteristics curve for the system was 0.970+/-0.006. The CAD system identified solid breast nodules with comparatively high accuracy in the different ultrasound systems investigated.

Computer-Aided Diagnosis of Breast Tumors with Different US Systems

Rationale and Objectives The authors performed this study to determine whether a computer-aided diagnostic (CAD) system was suitable from one ultrasound (US) unit to another after parameters were adjusted by using intelligent selection algorithms. Materials and Methods The authors used texture analysis and data mining with a decision tree model to classify breast tumors with different US systems. The databases of training cases from one unit and testing cases from another were collected from different countries. Regions of interest on US scans and co-variance texture parameters were used in the diagnosis system. Proposed adjustment schemes for different US systems were used to transform the information needed for a differential diagnosis. Results Comparison of the diagnostic system with and without adjustment, respectively, yielded the following results: accuracy, 89.9% and 82.2%; sensitivity, 94.6% and 92.2%; specificity, 85.4% and 72.3%; positive predictive value, 86.5% and 76.8%; and negative predictive value, 94.1% and 90.4%. The improvement in accuracy, specificity, and positive predictive value was statistically significant. Diagnostic performance was improved after the adjustment. Conclusion After parameters were adjusted by using intelligent selection algorithms, the performance of the proposed CAD system was better both with the same and with different systems. Different resolutions, different setting conditions, and different scanner ages are no longer obstacles to the application of such a CAD system.

Computer-aided diagnosis for surgical office-based breast ultrasound.

The computer-aided diagnostic system is an intelligent system with great potential for categorizing solid breast nodules. It can be used conveniently for surgical office-based digital ultrasonography (US) of the breast. Retrospective, nonrandomized study. University teaching hospital. We retrospectively reviewed 243 medical records of digital US images of the breast of pathologically proved, benign breast tumors from 161 patients (ie, 136 fibroadenomas and 25 fibrocystic nodules), and carcinomas from 82 patients (ie, 73 invasive duct carcinomas, 5 invasive lobular carcinomas, and 4 intraductal carcinomas). The digital US images were consecutively recorded from January 1, 1997, to December 31, 1998. The physician selected the region of interest on the digital US image. Then a learning vector quantization model with 24 autocorrelation texture features is used to classify the tumor as benign or malignant. In the experiment, 153 cases were arbitrarily selected to be the training set of the learning vector quantization model and 90 cases were selected to evaluate the performance. One experienced radiologist who was completely blind to these cases was asked to classify these tumors in the test set. Contribution of breast US to diagnosis. The performance comparison results illustrated the following: accuracy, 90%; sensitivity, 96.67%; specificity, 86.67%; positive predictive value, 78.38%; and negative predictive value, 98.11% for the computer-aided diagnostic (CAD) system and accuracy, 86.67%; sensitivity, 86.67%; specificity, 86.67%; positive predictive value, 76.47%; and negative predictive value, 92.86% for the radiologist. The proposed CAD system provides an immediate second opinion. An accurate preoperative diagnosis can be routinely established for surgical office-based digital US of the breast. The diagnostic rate was even better than the results of an experienced radiologist. The high negative predictive rate by the CAD system can avert benign biopsies. It can be easily implemented on existing commercial diagnostic digital US machines. For most available diagnostic digital US machines, all that would be required for the CAD system is only a personal computer loaded with CAD software.