5 research outputs found
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Purpose: To optimize the binary detection of Chronic Obstructive Pulmonary
Disease (COPD) based on emphysema presence in the lung with convolutional
neural networks (CNN) by exploring manually adjusted versus automated
window-setting optimization (WSO) on computed tomography (CT) images.
Methods: 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78
subjects (43 with COPD; 35 healthy controls) were selected retrospectively
(10.2018-12.2019) and preprocessed. For each image, intensity values were
manually clipped to the emphysema window setting and a baseline 'full-range'
window setting. Class-balanced train, validation, and test sets contained
3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing
various CNN architectures. Furthermore, automated WSO was implemented by adding
a customized layer to the model. The image-level area under the Receiver
Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] and
P-values calculated from one-sided Mann-Whitney U-test were utilized to compare
model variations.
Results: Repeated inference (n=7) on the test set showed that the DenseNet
was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85]
without WSO. Comparably, with input images manually adjusted to the emphysema
window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89]
(P=0.03). By adding a customized WSO layer to the DenseNet, an optimal window
in the proximity of the emphysema window setting was learned automatically, and
a mean AUC of 0.82 [0.78, 0.86] was achieved.
Conclusion: Detection of COPD with DenseNet models was improved by WSO of CT
data to the emphysema window setting range
Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging
Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image
Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection
The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA)
for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need
for improved diagnostic solutions. The primary objective of this study is to
leverage deep learning techniques to enhance the Computer Assisted Diagnosis
(CAD) of PE. With this aim, we propose a classifier-guided detection approach
that effectively leverages the classifier's probabilistic inference to direct
the detection predictions, marking a novel contribution in the domain of
automated PE diagnosis. Our classification system includes an Attention-Guided
Convolutional Neural Network (AG-CNN) that uses local context by employing an
attention mechanism. This approach emulates a human expert's attention by
looking at both global appearances and local lesion regions before making a
decision. The classifier demonstrates robust performance on the FUMPE dataset,
achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and an
F1-score of 0.805 with the Inception-v3 backbone architecture. Moreover, AG-CNN
outperforms the baseline DenseNet-121 model, achieving an 8.1% AUROC gain.
While previous research has mostly focused on finding PE in the main arteries,
our use of cutting-edge object detection models and ensembling techniques
greatly improves the accuracy of detecting small embolisms in the peripheral
arteries. Finally, our proposed classifier-guided detection approach further
refines the detection metrics, contributing new state-of-the-art to the
community: mAP, sensitivity, and F1-score of 0.846, 0.901, and 0.779,
respectively, outperforming the former benchmark with a significant 3.7%
improvement in mAP. Our research aims to elevate PE patient care by
integrating AI solutions into clinical workflows, highlighting the potential of
human-AI collaboration in medical diagnostics.Comment: Published in Expert Systems With Application