Computer-aided diagnosis tool for the detection of cancerous nodules in X-ray images

This thesis involves development of a computer-aided diagnosis (CAD) tool for the detection of cancerous nodules in X-ray images. Both cancerous and non-cancerous regions appear with little distinction on an X-ray image. For accurate detection of cancerous nodules, we need to differentiate the cancerous nodules from the non-cancerous. We developed an artificial neural network to differentiate them. Artificial neural networks (ANN) find a large application in the area of medical imaging. They work in a manner rather similar to the brain and have good decision making criteria when trained appropriately. We trained the neural network by the backpropagation algorithm and tested it with different images from a database of thoracic radiographs (chest X-rays) of dogs from the LSU Veterinary Medical Center. If we give X-ray images directly as input to the ANN, it incurs substantial complexity and training time for the network to process the images. A pre-processing stage involving some image enhancement techniques helps to solve the problem to a certain extent. The CAD tool developed in this thesis works in two stages. We pre-process the digitized images (by contrast enhancement, thresholding, filtering, and blob analysis) obtained after scanning the X-rays and then separate the suspected nodule areas (SNA) from the image by a segmentation process. We then input enhanced SNAs to the backpropagation-trained ANN. When given these enhanced SNAs, the neural network recognition accuracy, compared to unprocessed images as inputs, improved from 70% to 83.33%

Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning (DL) models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different for pediatrics across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to analyze the generalizability of deep adult lung segmentation models to the pediatric population and improve performance through a systematic combinatorial approach consisting of CXR modality-specific weight initializations, stacked generalization, and an ensemble of the stacked generalization models. Novel evaluation metrics consisting of Mean Lung Contour Distance and Average Hash Score are proposed in addition to the Multi-scale Structural Similarity Index Measure, Intersection of Union, and Dice metrics to evaluate segmentation performance. We observed a significant improvement (p < 0.05) in cross-domain generalization through our combinatorial approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.Comment: 11 pages, 7 figures, and 8 table