English

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities.

Computed tomography (CT) is commonly used as a diagnostic and treatment planning imaging modality in craniomaxillofacial (CMF) surgery to correct patient's bony defects. A major disadvantage of CT is that it emits harmful ionizing radiation to patients during the exam. Magnetic resonance imaging (MRI) is considered to be much safer and noninvasive, and often used to study CMF soft tissues (e.g., temporomandibular joint and brain). However, it is extremely difficult to accurately segment CMF bony structures from MRI since both bone and air appear to be black in MRI, along with low signal-to-noise ratio and partial volume effect. To this end, we proposed a 3D deep-learning based cascade framework to solve these issues. Specifically, a 3D fully convolutional network (FCN) architecture is first adopted to coarsely segment the bony structures. As the coarsely segmented bony structures by FCN tend to be thicker, convolutional neural network (CNN) is further utilized for fine-grained segmentation. To enhance the discriminative ability of the CNN, we particularly concatenate the predicted probability maps from FCN and the original MRI, and feed them together into the CNN to provide more context information for segmentation. Experimental results demonstrate a good performance and also the clinical feasibility of our proposed 3D deep-learning based cascade framework.

Segmentation is one of the most important tasks in medical image analysis. With the development of deep leaning, fully convolutional networks (FCNs) have become the dominant approach for this task and their extension to 3D achieved considerable improvements for automated organ segmentation in volumetric imaging data, such as computed tomography (CT). One popular FCN network architecture for 3D volumes is V-Net, originally proposed for single region segmentation. This network effectively solved the imbalance problem between foreground and background voxels by proposing a loss function based on the Dice similarity metric. In this work, we extend the depth of the original V-Net to obtain better features to model the increased complexity of multi-class segmentation tasks at higher input/output resolutions using modern large-memory GPUs. Furthermore, we markedly improved the training behaviour of V-Net by employing batch normalization layers throughout the network. In this way, we can efficiently improve the stability of the training optimization, achieving faster and more stable convergence. We show that our architectural changes and refinements dramatically improve the segmentation performance on a large abdominal CT dataset and obtain close to 90% average Dice score.

Based on Global Cancer 2015 statistics, the lung cancer of all types constitutes 27% of overall cancers while 19.5% of cancer deaths are due to lung cancer. In lieu of this, an effective lung cancer screening test using Computed Tomography (CT) scan is crucial to detect cancer at the early stage. The interpretation of the CT images requires an advanced CAD system of high accuracy for instance, in classifying the lung nodules. Recently, Deep Learning method that is Convolution Neural Network (CNN) shows an outstanding success in lung nodules classification. However, the training of CNN requires a great number of images. Such a requirement is an issue in the case of medical images. Generative adversarial network (GAN) has been introduced to generate new image datasets for CNN training. Thus, the main objective of this study is to compare the performance of CNN architectures with and without the implementation of GAN for lung nodules classification in CT images. Here, the study used Conditional GAN (cGAN) to generate benign nodules images. The classification accuracy of the combined cGAN-CNN architecture was compared among CNN pretraining networks namely GoogleNet, ShuffleNet, DenseNet, and MobileNet based on classification accuracy, specificity, sensitivity, and AUC-ROC values. The experiment was tested on LIDC-IDRI database. The results showed cGAN-CNN architecture improves the overall classification accuracy as compared to CNN alone with the cGAN-ShuffleNet architecture performed the best, achieving 98.38% accuracy, 98.13% specificity, 100% sensitivity and AUC-ROC at 99.90%. Overall, the classification performance of CNN can be improved by integrating GAN architecture to mitigate the constraint of having a large medical image dataset, in this case, CT lung nodules images.

Most of abdominal CT images include Gaussian noise, and CT scans form a blurry vision because of the internal fat tissue inside of abdomen. These two handicaps (noise and fat tissue) constitute an impediment in front of an accurate abdominal organ & tumour segmentation. Also segmentation techniques generally fall into error on segmentation of close grayscale regions. Therefore, denoising and enhancement parts are crucial for better segmentation results on CT images. In this paper, we form a tool including three efficient algorithms for the purpose of image enhancement before abdominal organ & tumour segmentation. At first, the denoising process is realized by Block Matching and 3D Filtering (BM3D) algorithm for elimination of Gaussian noise stated in arterial phase CT images. At second, Fast Linking Spiking Cortical Model (FL-SCM) is used for removing the internal fat tissue. At last, Otsu algorithm is processed to remove the redundant parts within the image. In experiments, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index are used to evaluate the performance of proposed method, and a visual comparison is presented. According to results, it is seen that proposed tool obtains the best PSNR and SSIM values in comparison with two steps of pipeline (FL-SCM and BM3D & FL-SCM). Consequently, BM3D & FL-SCM & Otsu (BFO) ensures a clean abdomen particularly for segmentation of liver, spleen, pancreas, adrenal tumours, aorta, ribs, spinal cord and kidneys.

Nowadays, medical imaging has an important role in radiotherapy and treatment planning process. Despite the increasing usage of the magnetic resonance imaging (MRI) in the external radiotherapy (RT) design process. MRI is only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). CT-scans are the main imaging modality in external beam radiotherapy. They allow the definition of tissue electron density necessary for dose calculation. A magnetic resonance (MR)-only radiotherapy workflow can reduce cost, radiation exposure and uncertainties. However, it requires the generation of pseudo CT from MRI images for patient setup and dose calculation. A crucial prerequisite is generating the so called pseudo-CT (pCT) images for accurate dose calculation and planning. Our proposed GAN (Generative Adversarial Network) method to generate pseudo CT images has been shown to provide pseudo CT images with excellent image quality. In this study, the main aim is to allow pseudo-CT images to be generated from MRI data for the diagnosis of bone lesions and investigate the accuracy of dose calculation in brain frameless Stereotactic Radio Surgery (SRS) using pseudo CT images which are generated from MRI images using the Deep Convolutional Neural network method. The outcome of a detailed assessment of various strategies for GAN, brain bone lesion identification from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a DCNN based GAN pseudo-CT generation approach.

Lung disease is a severe threat to the respiratory system and can be fatal if not treated immediately. Common examples include Pneumonia, Covid-19, Lung Cancer, and Tuberculosis. Early detection of lung disease is essential to prevent death. Medical imaging techniques, such as X-Ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI), play an essential role in diagnosing disease by providing visual information to radiologists and doctors. The use of the CNN architecture for lung disease detection has been increasing recently, with various types of CNN architectures being published separately. Thus, not all types of CNN architectures can be exposed. This literature review aims to identify and analyze the latest research on using CNN architecture in medical image analysis for lung disease detection. Forty-seven research articles posted from January 2019 to May 2023 were retained and selected for further exploration based on predefined inclusion and exclusion criteria. A systematic literature review is a method for identifying, evaluating, and interpreting all available study material to answer a research question. Analysis of the reviewed papers highlights the popularity of the CNN architecture for early detection of lung disease. Among the nineteen identified CNN architectural models, VGG-16 and ResNet50 emerged as the most commonly used for lung disease detection. Pneumonia and COVID-19 are among the lung diseases being studied due to their high global incidence rates, requiring early detection to expedite diagnosis and clinical decision-making. Chest X-ray emerged as the most widely used imaging modality for lung disease detection using CNN due to its accessibility, affordability, and non-invasiveness. While the reviewed studies show high accuracy, optimal performance may vary depending on the dataset, CNN architecture used, and other parameters.

The COVID-19 pandemic, also known as the coronavirus pandemic, is one of a major outbreak spreading across many countries around the world. It impacts severely on the health and life of many people all around the world. Medical imaging is a widely accepted technique for the early detection and diagnosis of disease that includes different techniques such as X-ray, computed tomography (CT) scan etc. For diagnosis COVID-19, chest X-ray is the imaging technique that plays an important role. In the recent years, deep neural networks have been successfully applied in many computer vision tasks including medical imaging. In this paper, we have experimented and evaluated DenseNet model for the classification of COVID-19 chest X-ray images. For that, a publicly available dataset contains 6432 chest X-ray images categorizes into 3 classes were used. Transfer learning and fine-tuning is applied for training the three variant of DenseNet model namely DenseNet121, DenseNet169 and DenseNet201. After evaluating the performance, it has been found that DenseNet201 achieved highest validation accuracy i.e. 0.9367 and lowest validation loss i.e. 0.1653 for classification of COVID-19 in chest X-ray images. © 2021 Karadeniz Technical University. All rights reserved.

Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies.

Deep learning models assist ophthalmologists in early detection of diseases from retinal images and timely treatment. Owing to robust and accurate results from deep learning models, we aim to use convolutional neural network (CNN) to provide a non-invasive method for early detection of eye diseases. We used a hybridized CNN with deep learning (DL) based on two separate CNN blocks, to identify multiple Optic Disc Cupping, Diabetic Retinopathy, Media Haze, and Healthy images. We used the RFMiD dataset, which contains various categories of fundus images representing different eye diseases. Data augmenting, resizing, coping, and one-hot encoding are used among other preprocessing techniques to improve the performance of the proposed model. Color fundus images have been analyzed by CNNs to extract relevant features. Two CCN models that extract deep features are trained in parallel. To obtain more noticeable features, the gathered features are further fused utilizing the Canonical Correlation Analysis fusion approach. To assess the effectiveness, we employed eight classification algorithms: Gradient boosting, support vector machines, voting ensemble, medium KNN, Naive Bayes, COARSE- KNN, random forest, and fine KNN. With the greatest accuracy of 93.39%, the ensemble learning performed better than the other algorithms. The accuracy rates suggest that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. It contributes to the field of eye disease detection through the analysis of color fundus images by providing a reliable and efficient diagnostic system.

Insurance fraud represents a significant financial burden globally, with annual losses exceeding $200 billion across healthcare, auto, and life insurance sectors. Traditional rule-based fraud detection systems have proven inadequate against increasingly sophisticated fraudulent schemes, prompting widespread adoption of deep learning (DL) approaches. This comprehensive review systematically examines the application of DL techniques to insurance fraud detection, analyzing 57 peer-reviewed studies published between 2019 and 2025. We evaluate the effectiveness of various architectures including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Graph Neural Networks (GNNs), and hybrid models across healthcare, auto, and life insurance domains. Our analysis reveals that ensemble methods combining CNNs with LSTMs achieve accuracies ranging from 89.6% to 98%, while GNN-based approaches demonstrate superior performance in detecting collusive fraud networks with accuracies exceeding 84%. The review identifies critical challenges including severe class imbalance with fraud rates of 0.03-3%, model interpretability requirements, and limited availability of labeled datasets. We examine emerging trends including explainable artificial intelligence (XAI) frameworks, attention mechanisms, generative adversarial networks (GANs) for synthetic data generation, and federated learning approaches for privacy-preserving fraud detection. This review contributes to understanding the current state-of-the-art in DL for insurance fraud detection while highlighting critical research gaps and future directions in model interpretability, cross-domain transfer learning, and real-time detection systems.

The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

Deep learning (DL) approaches have been applied in different sectors of medical imaging applications, i.e. classification, segmentation and detection tasks and shown superior performance. The DL based generative methods are used for image denoising, enhancement and restoration task. In case of image analysis, image denoising is one of the most crucial preprocessing steps. Recently, there are various DL approaches are applied in image denoising problems and achieved state-of-the-art performance. In this work, we apply recurrent residual U-Net (R2U-Net) based autoencoder model for medical image denoising which is applied for digital pathology, dermoscopy, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images denoising tasks. The performance of R2U-Net based autoencoder model is also evaluated for Transfer domain (TD) between MRI and CT scan images. The experiments have conducted on different publicly available medical image datasets and shows promising denoising results which can be applied in different medical imaging applications.

paper, we focus on exploring the relationship between weave patterns and their mechanical properties in woven fiber composites through Machine Learning. Specifically, we explore the interactions between woven architectures and in-plane stiffness properties through Deep Convolutional Neural Network (DCNN) and Generative Adversarial Network (GAN). Our research is important for exploring how woven composite’s pattern is related to its mechanical properties and accelerating woven composite design as well as optimization. We focus on two tasks: (1) Stiffness prediction: Predicting in-plane stiffness properties for given weave patterns. Our DCNN extracts high-level features through several convolutional and fully connected layers to determine the final predictions. (2) Weave pattern prediction: Predicting weave patterns for target stiffness properties, which can be treated as the reverse task of the first one. Due to many-to-one mapping between weave patterns and the composite properties, we utilize a Decoder Neural Network as our baseline model and compare its performance with GAN and Genetic Algorithm. We represent the weave patterns as 2D checkerboard models and use finite element analysis (FEA) to determine in-plane stiffness properties, which serve as input data for our ML framework. We show that: (1) for stiffness prediction, DCNN can predict stiffness values for a given weave pattern with relatively high accuracy (above 93%); (2) for weave pattern prediction, the GAN model gives the best prediction accuracy (above 92%) while Decoder Neural Network has the best time efficiency. HAOTIAN FENG

Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fragmented. In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e.g., random noise samples to diverse pathological images) or image-to-image GANs (e.g., a benign image to a malignant one). Yet, no research has reported results combining noise-to-image and image-to-image GANs for further performance boost. Therefore, to maximize the DA effect with the GAN combinations, we propose a two-step GAN-based DA that generates and refines brain Magnetic Resonance (MR) images with/without tumors separately: (i) Progressive Growing of GANs (PGGANs), multi-stage noise-to-image GAN for high-resolution MR image generation, first generates realistic/diverse 256&#x00D7;256 images; (ii) Multimodal UNsupervised Image-to-image Translation (MUNIT) that combines GANs/Variational AutoEncoders or SimGAN that uses a DA-focused GAN loss, further refines the texture/shape of the PGGAN-generated images similarly to the real ones. We thoroughly investigate CNN-based tumor classification results, also considering the influence of pre-training on ImageNet and discarding weird-looking GAN-generated images. The results show that, when combined with classic DA, our two-step GAN-based DA can significantly outperform the classic DA alone, in tumor detection (i.e., boosting sensitivity 93.67% to 97.48%) and also in other medical imaging tasks.