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Examples of 512 × 512 pixels images from dataset (a) Original CT slice image (b) respective ground truth
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Accurate vertebrae segmentation from medical images plays an important role in clinical tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease, spondylolisthesis, and post-operative assessment. Although the structures of bone have high contrast in medical images, vertebrae segmentation is a challenging task due to its...
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... deformation is correlated with a large value of Gaussian kernel, and the estimated transformation is used as a small kernel input of smoothness. Figure 2 shows 512 × 512 pixels original CT slice images with respective ground truths from dataset. ...
Context 2
... it can be concluded that our proposed OP-convNet framework is superior in terms of its patch-based classification performance. Figure 12 shows patch-based classification performance comparison. ...
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Citations
... Inspired by previous work, 1,2,4,[6][7][8][9][11][12][13][15][16][17]19,[22][23][24][25][26][27] we decided to use a U-shaped hybrid architecture like Trans-Unet to segment coronary vessels. Specifically, we adopt Segformer 15 as a hierarchical Transformer encoder to extract global context information. ...
Coronary artery three‐dimensional reconstruction is essential for preventing, diagnosing, and treating coronary heart disease. This study introduces SegUnet, a lightweight hybrid CNN‐Transformer network for pixel‐level segmentation of coronary artery computed tomography angiography (CTA) slices to enhance the precision of coronary artery reconstruction. The overall SegUnet adopts a U‐shaped encoder‐decoder structure, with a hierarchical Transformer structure serving as the encoder to extract global contextual information; the decoder is enhanced with spatial attention mechanism (SAM) and residual structure to improve its perception and adaptive adjustment abilities for coronary artery vessels. In contrast to other methods, we propose an interpolation‐based multi‐feature fusion bridge to integrate multi‐scale features between encoder levels, capturing their semantic dependencies. Experimental results show that SegUnet achieves a prediction accuracy of 97.23% on the test set of coronary arteries 2D slices. Moreover, the network exhibits excellent performance on Synapse Multi‐Organ Segmentation, highlighting the superiority, effectiveness, and robustness of our proposed method. The outstanding performance of SegUnet in coronary artery segmentation has elevated the quality of three‐dimensional reconstruction of the coronary arteries, exerting a positive impact on the enhancement of diagnosis and treatment of coronary artery diseases.
... There are also many studies of vertebral body segmentation based on CT (Computerised Tomography) that have been investigated in recent years. Qadri et al. 24 proposed OP-convNet, which is an overlapping patch-based model, for automatic vertebrae CT image segmentation. They employed overlapping patches in segmentation tasks using 2D convNet in order to reduce memory usage and the risk of overfitting. ...
Accurate annotation of vertebral bodies is crucial for automating the analysis of spinal X-ray images. However, manual annotation of these structures is a laborious and costly process due to their complex nature, including small sizes and varying shapes. To address this challenge and expedite the annotation process, we propose an ensemble pipeline called VertXNet. This pipeline currently combines two segmentation mechanisms, semantic segmentation using U-Net, and instance segmentation using Mask R-CNN, to automatically segment and label vertebral bodies in lateral cervical and lumbar spinal X-ray images. VertXNet enhances its effectiveness by adopting a rule-based strategy (termed the ensemble rule) for effectively combining segmentation outcomes from U-Net and Mask R-CNN. It determines vertebral body labels by recognizing specific reference vertebral instances, such as cervical vertebra 2 (‘C2’) in cervical spine X-rays and sacral vertebra 1 (‘S1’) in lumbar spine X-rays. Those references are commonly relatively easy to identify at the edge of the spine. To assess the performance of our proposed pipeline, we conducted evaluations on three spinal X-ray datasets, including two in-house datasets and one publicly available dataset. The ground truth annotations were provided by radiologists for comparison. Our experimental results have shown that the proposed pipeline outperformed two state-of-the-art (SOTA) segmentation models on our test dataset with a mean Dice of 0.90, vs. a mean Dice of 0.73 for Mask R-CNN and 0.72 for U-Net. We also demonstrated that VertXNet is a modular pipeline that enables using other SOTA model, like nnU-Net to further improve its performance. Furthermore, to evaluate the generalization ability of VertXNet on spinal X-rays, we directly tested the pre-trained pipeline on two additional datasets. A consistently strong performance was observed, with mean Dice coefficients of 0.89 and 0.88, respectively. In summary, VertXNet demonstrated significantly improved performance in vertebral body segmentation and labeling for spinal X-ray imaging. Its robustness and generalization were presented through the evaluation of both in-house clinical trial data and publicly available datasets.
... Compared to traditional methods, deep learning-based liver segmentation method is a data-driven approach (Furqan Qadri et al. 2019;Qadri et al. 2019Qadri et al. , 2021) that allows end-to-end optimization without manual feature engineering (Litjens et al. 2017). Many of the early deep learning-based liver segmentation methods combined neural networks with specialized post-processing routines. ...
Introduction
The automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels.
Methods
To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training.
Results and discussion
Based on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.
... Besides, recent advances in medical diagnosis have witnessed a surge in the adoption of deep learning methods. Studies such as Ahmad, Ding, et al., 2019;Alzghoul et al., 2023;Bataineh, 2019;Doppala et al., 2023;Furqan Qadri et al., 2019;Habib & Qureshi, 2023;Hirra et al., 2021;Qadri et al., 2019;Qadri et al., 2021;Qadri et al., 2022;Qadri et al., 2023;Vamsi et al., 2022) have demonstrated deep learning ability in dealing with large datasets and complex patterns. While deep learning methods provide robust performance Arora et al., 2022;Jalali, Arora, Panigrahi, et al., 2022;Jalali, Osorio, Ahmadian, et al., 2022;Mehnatkesh et al., 2023;Saffari et al., 2023), their computational density and the need for extensive data and training can be limited in certain scenarios. ...
Integrating machine learning techniques into medical diagnostic systems holds great promise for enhancing disease identification and treatment. Among the various options for training such systems, the extreme learning machine (ELM) stands out due to its rapid learning capability and computational efficiency. However, the random selection of input weights and hidden neuron biases in the ELM can lead to suboptimal performance. To address this issue, our study introduces a novel approach called modified Harris hawks optimizer (MHHO) to optimize these parameters in ELM for medical classification tasks. By applying the MHHO‐based method to seven medical datasets, our experimental results demonstrate its superiority over seven other evolutionary‐based ELM trainer models. The findings strongly suggest that the MHHO approach can serve as a valuable tool for enhancing the performance of ELM in medical diagnosis.
... Consequently, these conventional object detection algorithms suffer from long execution times, complex feature extraction modules, high computational costs, low robustness of extracted features, and low recognition accuracy. In contrast, deep learning-based convolutional neural networks extract image features incrementally and learn feature information independently, resulting in a higher accuracy and smaller models [6,7]. Therefore, in recent years, deep learning-based convolutional neural networks have been widely used in the fields of foreign object and defect detection. ...
This study addresses the challenges that conventional network models face in detecting small foreign objects on industrial production lines, exemplified by scenarios where a single piece of iron filing occupies approximately 0.002% of the image area. To tackle this, we introduce an enhanced YOLOv8-MeY model for detecting foreign objects on the surface of sugar bags. Firstly, the introduction of a 160 × 160-scale small object detection layer and integration of the Global Attention Mechanism (GAM) attention module into the feature fusion network (Neck) increased the network’s focus on small objects. This enhancement improved the network’s feature extraction and fusion capabilities, which ultimately increased the accuracy of small object detection. Secondly, the model employs the lightweight network GhostNet, replacing YOLOv8’s principal feature extraction network, DarkNet53. This adaptation not only diminishes the quantity of network parameters but also augments feature extraction capabilities. Furthermore, we substituted the Bottleneck in the C2f of the YOLOv8 model with the Spatial and Channel Reconstruction Convolution (SCConv) module, which, by mitigating the spatial and channel redundancy inherent in standard convolutions, reduced computational demands while elevating the performance of the convolutional network model. The model has been effectively applied to the automated sugar dispensing process in food factories, exhibiting exemplary performance. In detecting diverse foreign objects like 2 mm iron filings, 7 mm wires, staples, and cockroaches, the YOLOv8-MeY model surpasses the Faster R-CNN model and the contemporaneous YoloV8n model of equivalent parameter scale across six metrics: precision, recall, mAP@0.5, parameters, GFLOPs, and model size. Through 400 manual placement tests involving four types of foreign objects, our statistical results reveal that the model achieves a recognition rate of up to 92.25%. Ultimately, we have successfully deployed this model in automated sugar bag dispensing scenarios.
... We used six metrics to evaluate the performance of the proposed W_CVNet model: accuracy, specificity, sensitivity, precision, recall, and F1-score [36,37]. ...
The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method was used for feature extraction and classification recognition. A dual asymmetric complementary bilinear feature extraction method (D-CBM) was used to fully extract complementary features, which solved the problem of insufficient feature extraction by a single deep learning network. Third, an unsupervised learning method based on Fuzzy C-Means (FCM) clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease severity. In this study, 5-fold cross-validation methods were used, and the results showed that the network had an average classification accuracy of 85.8%, outperforming six recent advanced classification models. W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and, in the case of COVID-19 patients, to further predict the area of the lesion.
... Among them, 5 were conventional deep-learning models that include CNN, 38 and various variants of RNN like long short-term memory (LSTM), 39 bidirectional LSTM, gated recurrent units (GRU), 40 and bidirectional GRU. 41 The other six were state-of-the-art deep-learning technologies that include deep belief network (DBN), 42 Patch-based DBN (Pa-DBN), 43,44 OP-ConvNet, 45 Gaussian-weight initialization of CNN (Ga-CNN), 46 stacked sparse autoencoder (SSAE), [47][48][49] and deep stacked auto encoder (DSAE). 50 A significant amount of time was taken to determine each model's architecture, which gives the best performance. ...
Infants are vulnerable to several health problems and cannot express their needs clearly. Whenever they are in a state of urgency and require immediate attention, they cry, which is a form of communication for them. Therefore, the parents of the infants always need to be alert and keep continuous supervision of their infants. However, parents cannot monitor their infants all the time. An infant monitoring system could be a possible solution to monitor the infants, determine when the infants are crying, and notify the parents immediately. Although many such systems are available, most cannot detect infant cries. Some systems have infant cry detection mechanisms, but those mechanisms are not very accurate in detecting infant cries because the mechanisms either include obsolete approaches or machine learning (ML) models that cannot identify infant cries from noisy household settings. To address this limitation, in this research, different conventional and hybrid ML models were developed and analyzed in detail to find out the best model for detecting infant cries in a household setting. A stacked classifier is proposed using different state‐of‐the‐art technologies, outperforming all other developed models. The proposed CNN‐SCNet's (CNN‐Stacked Classifier Network) precision, recall, and f1‐score were found to be 98.72%, 98.05%, and 98.39%, respectively. Infant monitoring systems can use this classifier to detect infant cries in noisy household settings.
... We have chosen a series of evaluation metrics to assess the proposed model's efficacy objectively. These indicators provide insights into the model's various classification facets [48,49]. ...
The rise of vision-threatening diabetic retinopathy (VTDR) underscores the imperative for advanced and efficient early detection mechanisms. With the integration of the Internet of Things (IoT) and 5G technologies, there is transformative potential for VTDR diagnosis, facilitating real-time processing of the burgeoning volume of fundus images (FIs). Combined with artificial intelligence (AI), this offers a robust platform for managing vast healthcare datasets and achieving unparalleled disease detection precision. Our study introduces a novel AI-driven VTDR detection framework that integrates multiple models through majority voting. This comprehensive approach encompasses pre-processing, data augmentation, feature extraction using a hybrid convolutional neu-ral network-singular value decomposition (CNN-SVD) model, and classification through an enhanced SVM-RBF combined with a decision tree (DT) and K-nearest neighbor (KNN). Validated on the IDRiD dataset, our model boasts an accuracy of 99.89%, a sensitivity of 84.40%, and a specificity of 100%, marking a significant improvement over the traditional method. The convergence of the IoT, 5G, and AI technologies herald a transformative era in healthcare, ensuring timely and accurate VTDR diagnoses, especially in geographically underserved regions.
... Exposure of modern DL flourished in medical imaging in 2010s, and it was explored in different applications including image classification (Esteva et al., 2017;Lakhani & Sundaram, 2017;Qadri et al., 2021), detection (Panwar et al., 2020;Wu et al., 2021;Yao et al., 2021), segmentation (Gordienko et al., 2018;Hooda et al., 2019;Işın et al., 2016;Nadeem et al., 2021), registration (Eppenhof & Pluim, 2018a;Haskins et al., 2020), and enhancement (Guha et al., 2020(Guha et al., , 2021You et al., 2020) as well as in various research areas related to different diseases and anatomic body regions such as cancer screenings (Ardila et al., 2019;Esteva et al., 2017;L. Hu, Bell, et al., 2019), diabetic retinopathy and glaucoma detection (Gargeya & Leng, 2017;Gulshan et al., 2016), ultrasound detection of breast nodules (McKinney et al., 2020;Wang et al., 2016), and pulmonary nodules (Hamidian et al., 2017;Jin et al., 2018) while examples of segmentation applications include heart (Baumgartner et al., 2017;Ngo et al., 2017;Poudel et al., 2016), brain and brain tissues (Akkus et al., 2017;Chen et al., 2017;Ciresan et al., 2012;Işın et al., 2016;Stollenga et al., 2015;G. ...
Over the last decade, deep learning (DL) has contributed to a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID‐19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This article presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL‐based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.
This article is categorized under: Application Areas > Health Care
Technologies > Artificial Intelligence
Technologies > Computational Intelligence
Application Areas > Science and Technology
... The literature further delivers the various clinical decision support platforms, which used the DL-based medical image segmentation technique very efficiently in, for example, breast cancer classification [15,16]. Similarly, few studies provide a better solution for patch classification in CT scan images [17,18]. ...
The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body’s internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size).
