Browsing by Subject "Computer-aided diagnosis"

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Open Access
Artificial intelligence-based hybrid anomaly detection and clinical decision support techniques for automated detection of cardiovascular diseases and Covid-19
(2023-10) Terzi, Merve Begüm
Coronary artery diseases are the leading cause of death worldwide, and early diagnosis is crucial for timely treatment. To address this, we present a novel automated arti cial intelligence-based hybrid anomaly detection technique com posed of various signal processing, feature extraction, supervised, and unsuper vised machine learning methods. By jointly and simultaneously analyzing 12-lead electrocardiogram (ECG) and cardiac sympathetic nerve activity (CSNA) data, the automated arti cial intelligence-based hybrid anomaly detection technique performs fast, early, and accurate diagnosis of coronary artery diseases. To develop and evaluate the proposed automated arti cial intelligence-based hybrid anomaly detection technique, we utilized the fully labeled STAFF III and PTBD databases, which contain 12-lead wideband raw recordings non invasively acquired from 260 subjects. Using the wideband raw recordings in these databases, we developed a signal processing technique that simultaneously detects the 12-lead ECG and CSNA signals of all subjects. Subsequently, using the pre-processed 12-lead ECG and CSNA signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of coronary artery diseases. Using the extracted discriminative features, we developed a supervised classi cation technique based on arti cial neural networks that simultaneously detects anomalies in the 12-lead ECG and CSNA data. Furthermore, we developed an unsupervised clustering technique based on the Gaussian mixture model and Neyman-Pearson criterion that performs robust detection of the outliers corresponding to coronary artery diseases. By using the automated arti cial intelligence-based hybrid anomaly detection technique, we have demonstrated a signi cant association between the increase in the amplitude of CSNA signal and anomalies in ECG signal during coronary artery diseases. The automated arti cial intelligence-based hybrid anomaly de tection technique performed highly reliable detection of coronary artery diseases with a sensitivity of 98.48%, speci city of 97.73%, accuracy of 98.11%, positive predictive value (PPV) of 97.74%, negative predictive value (NPV) of 98.47%, and F1-score of 98.11%. Hence, the arti cial intelligence-based hybrid anomaly detection technique has superior performance compared to the gold standard diagnostic test ECG in diagnosing coronary artery diseases. Additionally, it out performed other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it signi cantly increases the detec tion performance of coronary artery diseases by taking advantage of the diversity in di erent data types and leveraging their strengths. Furthermore, its perfor mance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or clas sify coronary artery diseases. It also has a very short implementation time, which is highly desirable for real-time detection of coronary artery diseases in clinical practice. The proposed automated arti cial intelligence-based hybrid anomaly detection technique may serve as an e cient decision-support system to increase physicians' success in achieving fast, early, and accurate diagnosis of coronary artery diseases. It may be highly bene cial and valuable, particularly for asymptomatic coronary artery disease patients, for whom the diagnostic information provided by ECG alone is not su cient to reliably diagnose the disease. Hence, it may signi cantly improve patient outcomes, enable timely treatments, and reduce the mortality associated with cardiovascular diseases. Secondly, we propose a new automated arti cial intelligence-based hybrid clinical decision support technique that jointly analyzes reverse transcriptase polymerase chain reaction (RT-PCR) curves, thorax computed tomography im ages, and laboratory data to perform fast and accurate diagnosis of Coronavirus disease 2019 (COVID-19). For this purpose, we retrospectively created the fully labeled Ankara University Faculty of Medicine COVID-19 (AUFM-CoV) database, which contains a wide variety of medical data, including RT-PCR curves, thorax computed tomogra phy images, and laboratory data. The AUFM-CoV is the most comprehensive database that includes thorax computed tomography images of COVID-19 pneu monia (CVP), other viral and bacterial pneumonias (VBP), and parenchymal lung diseases (PLD), all of which present signi cant challenges for di erential diagnosis. We developed a new automated arti cial intelligence-based hybrid clinical de cision support technique, which is an ensemble learning technique consisting of two preprocessing methods, long short-term memory network-based deep learning method, convolutional neural network-based deep learning method, and arti cial neural network-based machine learning method. By jointly analyzing RT-PCR curves, thorax computed tomography images, and laboratory data, the proposed automated arti cial intelligence-based hybrid clinical decision support technique bene ts from the diversity in di erent data types that are critical for the reliable detection of COVID-19 and leverages their strengths. The multi-class classi cation performance results of the proposed convolu tional neural network-based deep learning method on the AUFM-CoV database showed that it achieved highly reliable detection of COVID-19 with a sensitivity of 91.9%, speci city of 92.5%, precision of 80.4%, and F1-score of 86%. There fore, it outperformed thorax computed tomography in terms of the speci city of COVID-19 diagnosis. Moreover, the convolutional neural network-based deep learning method has been shown to very successfully distinguish COVID-19 pneumonia (CVP) from other viral and bacterial pneumonias (VBP) and parenchymal lung diseases (PLD), which exhibit very similar radiological ndings. Therefore, it has great potential to be successfully used in the di erential diagnosis of pulmonary dis eases containing ground-glass opacities. The binary classi cation performance results of the proposed convolutional neural network-based deep learning method showed that it achieved a sensitivity of 91.5%, speci city of 94.8%, precision of 85.6%, and F1-score of 88.4% in diagnosing COVID-19. Hence, it has compara ble sensitivity to thorax computed tomography in diagnosing COVID-19. Additionally, the binary classi cation performance results of the proposed long short-term memory network-based deep learning method on the AUFM-CoV database showed that it performed highly reliable detection of COVID-19 with a sensitivity of 96.6%, speci city of 99.2%, precision of 98.1%, and F1-score of 97.3%. Thus, it outperformed the gold standard RT-PCR test in terms of the sensitivity of COVID-19 diagnosis Furthermore, the multi-class classi cation performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database showed that it diagnosed COVID-19 with a sen sitivity of 66.3%, speci city of 94.9%, precision of 80%, and F1-score of 73%. Hence, it has been shown to very successfully perform the di erential diagnosis of COVID-19 pneumonia (CVP) and other pneumonias. The binary classi cation performance results of the automated arti cial intelligence-based hybrid clinical decision support technique revealed that it diagnosed COVID-19 with a sensi tivity of 90%, speci city of 92.8%, precision of 91.8%, and F1-score of 90.9%. Therefore, it exhibits superior sensitivity and speci city compared to laboratory data in COVID-19 diagnosis. The performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database demon strate its ability to provide highly reliable diagnosis of COVID-19 by jointly ana lyzing RT-PCR data, thorax computed tomography images, and laboratory data. Consequently, it may signi cantly increase the success of physicians in diagnosing COVID-19, assist them in rapidly isolating and treating COVID-19 patients, and reduce their workload in daily clinical practice.
Open Access
Computer aided diagnosis in radiology
(1999) Gürcan, Metin Nafi
Breast cancer is one of the most deadly diseases for middle-aged women. In this thesis, computer-aided diagnosis tools are developed for the detection of breast cancer on mammograms. These tools include a detection scheme for microcalcification clusters which are an early sign of breast cancer, and a method to detect the boundaries of mass lesions. In the first microcalcification detection method we propose, a subband decomposition structure is employed. Contrary to the previous work, the detection is carried out in the subband domain. The mammogram image is first processed by a subband decomposition filter bank. The resulting subimage is analyzed to detect microcalcification clusters. In regions corresponding to the healthy breast tissue the distribution is almost Gaussian. Since microcalcifications are small, isolated bright spots, they produce outliers in the subimages and the distribution of pixels deviates from Gaussian. The subimages are divided into overlapping square regions. In each square region, skewness and kurtosis values are estimated. As third and fourth order correlation parameters, skewness and kurtosis, are measures of the asymmetry and impulsiveness of the distribution, they can be used to find the locations of microcalcification clusters. If the values of these parameters are higher than experimentally determined thresholds then the region is marked as a potential cancer area. Experimental studies indicate that this method successfully detects regions containing microcalcifications. We also propose another microcalcification detection method which uses two- dimensional (2-D) adaptive filtering and a higher order statistics based Gaussianity test. In this method, statistics of the prediction errors are computed to determine whether the samples are from a Gaussian distribution. The prediction error sequence deviates from Gaussianity around microcalcification locations because prediction of microcalcification pixels is more difficult than prediction of the pixels corresponding to healthy breast tissue. Then, we develop a new Gaussianity test which has higher sensitivity to outliers. The scheme which uses this test gives better detection performance compared to the previously proposed methods. Within the detected regions it is possible to segment individual microcalcifications. An outlier labeling and nonlinear subband decomposition based microcalcification segmentation method is also investigated. Two types of lesions, namely mass and stellate lesions, might be indicators of breast cancer. Finally, we propose a snake algorithm based scheme to detect the boundaries of mass lesions on mammograms. This scheme is compared with a recently developed region growing based boundary detection method. It is observed that the snake algorithm results in a more smooth boundary which is consistent with the morphological structure of mass lesions.
Open Access
Detection and classification of breast cancer in whole slide histopathology images using deep convolutional networks
(2016-07) Geçer, Barış
The most frequent non-skin cancer type is breast cancer which is also named one of the most deadliest diseases where early and accurate diagnosis is critical for recovery. Recent medical image processing researches have demonstrated promising results that may contribute to the analysis of biopsy images by enhancing the understanding or by revealing possible unhealthy tissues during diagnosis. However, these studies focused on well-annotated and -cropped patches, whereas a fully automated computer-aided diagnosis (CAD) system requires whole slide histopathology image (WSI) processing which is, in fact, enormous in size and, therefore, difficult to process with a reasonable computational power and time. Moreover, those whole slide biopsies consist of healthy, benign and cancerous tissues at various stages and thus, simultaneous detection and classiffication of diagnostically relevant regions are challenging. We propose a complete CAD system for efficient localization and classification of regions of interest (ROI) in WSI by employing state-of-the-art deep learning techniques. The system is developed to resemble organized work ow of expert pathologists by means of progressive zooming into details, and it consists of two separate sequential steps: (1) detection of ROIs in WSI, (2) classification of the detected ROIs into five diagnostic classes. The novel saliency detection approach intends to mimic efficient search patterns of experts at multiple resolutions by training four separate deep networks with the samples extracted from the tracking records of pathologists' viewing of WSIs. The detected relevant regions are fed to the classification step that includes a deeper network that produces probability maps for classes, followed by a post-processing step for final diagnosis In the experiments with 240 WSI, the proposed saliency detection approach outperforms a state-of-the-art method by means of both efficiency and eectiveness, and the final classification of our complete system obtains slightly lower accuracy than the mean of 45 pathologists' performance. According to the Mc- Nemar's statistical tests, we cannot reject that the accuracies of 32 out of 45 pathologists are not different from the proposed system. At the end, we also provide visualizations of our deep model with several advanced techniques for better understanding of the learned features and the overall information captured by the network
Open Access
Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis
(The Institution of Engineering and Technology, 2002-10-24) Bagci, A. M.; Çetin, A. Enis
A method for computer-aided diagnosis of microcalcification clusters in mammogram images is presented. Microcalcification clusters which are an early sign of breast cancer appear as isolated bright spots in mammograms. Therefore they correspond to local maxima of the image. The local maxima of the image is first detected and they are ranked according to a higher-order statistical test performed over the subband domain data.