AI Medical Compendium Topic:
Radiographic Image Interpretation, Computer-Assisted

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A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules.

Asian Pacific journal of cancer prevention : APJCP
In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on early diagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network (DCNN) method is used for feature extrac...

Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.

Journal of biomedical informatics
We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic...

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network.

Computational and mathematical methods in medicine
Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvat...

ECM-CSD: An Efficient Classification Model for Cancer Stage Diagnosis in CT Lung Images Using FCM and SVM Techniques.

Journal of medical systems
As is eminent, lung cancer is one of the death frightening syndromes among people in present cases. The earlier diagnosis and treatment of lung cancer can increase the endurance rate of the affected people. But, the structure of the cancer cell makes...

RAMS: Remote and automatic mammogram screening.

Computers in biology and medicine
About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, the...

Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.

Journal of healthcare engineering
Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulm...

Learning to detect chest radiographs containing pulmonary lesions using visual attention networks.

Medical image analysis
Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires very large amounts of manually annotated radiographs, which are difficult to obtain. The increasing av...

Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset.

Biomedical engineering online
BACKGROUND: Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT ima...