Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 15 of 15 for “"Computer Aided Diagnosis (CAD)"”.
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Computer aided diagnosis of miliary TB in chest X-rays
With the improvement in computer technology, Computer Aided Diagnosis (CAD) is becoming an increasingly more powerful tool for radiologists. The focus of this project was on CAD of pulmonary miliary tuberculosis. Several methods for enhancing lung textures were discussed as an aid to the …
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SHAPE INFLUENCE IN MEDICAL IMAGE SEGMENTATION WITH APPLICATION IN COMPUTER AIDED DIAGNOSIS IN CT COLONOGRAPHY
Computer-aided diagnosis (CAD) is a procedure in medicine that assists radiologists or physicians in the interpretation of medical images. The application of CAD in screening colorectal cancer (CRC) has been studied for more than two decades. CRC is the second most deadly form of cancer in men and …
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Implementation and evaluation of a bony structure suppression software tool for chest X-ray imaging
… in the analysis of chest X-ray images. The diagnosis of pulmonary tuberculosis (TB) often includes the evaluation of chest X-ray images, and the reliability of image interpretation depends upon the experience of the radiologist. Computer-aided diagnosis (CAD) may be used to increase the …
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SVM-based Harris Corner Detection to Classify Normal/Abnormal Breast Mammogram Images
… is one of the most important materials of the Computer-Aided Diagnosis (CAD) system to support diagnosis of breast cancer. In the CAD system, intensity value is a widely used feature for medical image processing. Classification of the breast mammogram image as normal or abnormal class is …
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LUNG PATTERN CLASSIFICATION VIA DCNN
… of ILD plays a crucial role in the diagnosis and treatment process. In this research work, we disclose a lung nodules recognition method based on a deep convolutional neural network (DCNN) and global features, which can be used for computer-aided diagnosis (CAD) of global features of …
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Computer-aided diagnosis of tuberculosis in paediatric chest X-rays using local textural analysis
This report presents a computerised tool to analyse the appearance of the lung fields in paediatric chest X-rays to detect the presence of tuberculosis. The computer aided diagnosis (CAD) tool consists of 4 phases: 1) lung field segmentation; 2) lung field subdivision; 3) feature extraction and 4) …
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Lung cancer malignancy predication with recurrent neural networks
… 25% of all cancer deaths. Existing work in computer-aided diagnosis (CAD) has applied convolutional neural networks (CNNs) to detect and classify nodules in CT scans, with the goal of assisting radiologists diagnose lung cancer. In the past decade, new screening pro- tocols have been enacted …
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Detection of breast cancer microcalcifications in digitized mammograms. Developing segmentation and classification techniques for the processing of MIAS database mammograms based on the Wavelet Decomposition Transform and Support Vector Machines
Mammography is used to aid early detection and diagnosis systems. It takes an x-ray image of the breast and can provide a second opinion for radiologists. The earlier detection is made, the better treatment works. Digital mammograms are dealt with by Computer Aided Diagnosis (CAD) systems that can …
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Detection of breast cancer microcalcifications in digitized mammograms. Developing segmentation and classification techniques for the processing of MIAS database mammograms based on the Wavelet Decomposition Transform and Support Vector Machines.
Mammography is used to aid early detection and diagnosis systems. It takes an x-ray image of the breast and can provide a second opinion for radiologists. The earlier detection is made, the better treatment works. Digital mammograms are dealt with by Computer Aided Diagnosis (CAD) systems that can …
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Chest X-Ray Image Classification with Deep Learning
Computer-aided diagnosis (CAD) systems have been successfully helped to clinical diagnosis. This dissertation considers one essential task in CAD, the chest X-ray (CXR) image classification problem, with the deep learning technologies from the following three aspects. First, considering most …
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MITIGATING DATA SCARCITY CHALLENGES IN MEDICAL IMAGING ANALYSIS:ADVANCED LEARNING APPROACHES WITH EMPHASIS ON HEMOPHILIC ULTRASOUND IMAGES
… point-of-care) and enhance the capabilities of computer-aided diagnosis (CAD) systems. However, the lack of labeled training data makes the effective utilization of DL techniques in the medical domain impractical, leading to suboptimal performance in various imaging tasks, such as …
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Computer-Aided Detection of Clinically Significant Prostate Cancer using Bi-Parametric Magnetic Resonance Imaging
… These issues raise the need of developing computer-aided diagnosis (CAD) systems to support radiologists in the automatic detection of PCa on MRI. With the increasing of large available datasets, convolutional neural networks (CNNs) have become extensively applied in PCa detection, while …
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Image analysis methods for diagnosis of diffuse lung disease in multi-detector computed tomography
… analysis techniques have been broadly used in computer aided diagnosis tasks in recent years. Computer-aided image analysis is a popular tool in medical imaging research and practice, especially due to the development of different imag- ing modalities and due to the increased volume of image …
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Computer-Aided Diagnosis Systems in the Classification of Neuroblastoma Histological Images
… is considered the gold standard, computers can help to extract many more features, some of which may not be recognisable by the human eye. Neuroblastoma histological images have a complex texture with complicated features which are different from appearance-based features. …
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Endoscopy-focused primary, secondary and tertiary prevention of colorectal cancer
… then used as a template for the development of a computer-aided diagnosis (CAD) system that could enable expert-level accuracy for any endoscopist. A CAD system was created, learning from 1,235 colorectal images, and tested with data from two different centres (Australia and Japan) and imaging …