Medical Image Diagnosis Research Articles

TS-GCN: A novel tumor segmentation method integrating transformer and GCN.

As one of the critical branches of medical image processing, the task of segmentation of breast cancer tumors is of great importance for planning surgical interventions, radiotherapy and chemotherapy. Breast cancer tumor segmentation faces several challenges, including the inherent complexity and heterogeneity of breast tissue, the presence of various imaging artifacts and noise in medical images, low contrast between the tumor region and healthy tissue, and inconsistent size of the tumor region. Furthermore, the existing segmentation methods may not fully capture the rich spatial and contextual information in small-sized regions in breast images, leading to suboptimal performance. In this paper, we propose a novel breast tumor segmentation method, called the transformer and graph convolutional neural (TS-GCN) network, for medical imaging analysis. Specifically, we designed a feature aggregation network to fuse the features extracted from the transformer, GCN and convolutional neural network (CNN) networks. The CNN extract network is designed for the image's local deep feature, and the transformer and GCN networks can better capture the spatial and context dependencies among pixels in images. By leveraging the strengths of three feature extraction networks, our method achieved superior segmentation performance on the BUSI dataset and dataset B. The TS-GCN showed the best performance on several indexes, with Acc of 0.9373, Dice of 0.9058, IoU of 0.7634, F1 score of 0.9338, and AUC of 0.9692, which outperforms other state-of-the-art methods. The research of this segmentation method provides a promising future for medical image analysis and diagnosis of other diseases.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In the field of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity. Locating the defective cells precisely during the diagnosis phase helps to fight the greatest exterminator of mankind. Early detection of these defective cells requires an accurate computer-aided diagnostic system (CAD) that supports early treatment and promotes survival rates of patients. An earlier version of CAD systems relies greatly on the expertise of radiologist and it consumed more time to identify the defective region. The manuscript takes the efficacy of coalescing features like intensity, shape, and texture of the magnetic resonance image (MRI). In the Enhanced Feature Fusion Segmentation based classification method (EEFS) the image is enhanced and segmented to extract the prominent features. To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern (EnLBP), Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients (PGLCMHOG), and iGrab cut method to segment image. These prominent features along with deep features are coalesced to provide a single-dimensional feature vector that is effectively used for prediction. The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the generated vector. The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

Application for Diagnosis of Medical Imaging Using AI Techniques

Application for Diagnosis of Medical Imaging Using AI Techniques

Efficient Utilization of Image Fusion and Interpolation for Medical Image Diagnosis applications

Efficient Utilization of Image Fusion and Interpolation for Medical Image Diagnosis applications

Retracted: Medical Imaging Diagnosis of Anterior Cruciate Ligament Injury Based on Intelligent Finite-Element Algorithm.

[This retracts the article DOI: 10.1155/2021/6073757.].

Convolutional Neural Networks for Medical Image Diagnosis and Prognosis

One of the most incredible machine learning methods is deep learning. Utilised for picture categorization, clinical archiving, item identification, and other purposes. The quantity of medical image archives is expanding at an alarming rate as hospitals employ digital photos for documentation more frequently. Digital imaging is essential for assessing the severity of a patient's illness. Medical imaging has a wide variety of uses in research and diagnostics. Due to recent developments in image processing technology, self-operating identification of medical photos is still a research area for computer vision researchers. We require an appropriate classifier in order to categorise medical photos using various classifiers. After organ prediction and classification, the research was modified to include medical picture recognition. For medical picture detection, pretrained convolutional networks and Kmean clustering techniques similar to those used for organ identification are employed. Separating the training from the test data allowed for the data's authentication. The application of this strategy has been proven to be most effective for categorising various medical images of human organs.

Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process.

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.

A Brief Analysis of Multimodal Medical Image Fusion Techniques

Recently, image fusion has become one of the most promising fields in image processing since it plays an essential role in different applications, such as medical diagnosis and clarification of medical images. Multimodal Medical Image Fusion (MMIF) enhances the quality of medical images by combining two or more medical images from different modalities to obtain an improved fused image that is clearer than the original ones. Choosing the best MMIF technique which produces the best quality is one of the important problems in the assessment of image fusion techniques. In this paper, a complete survey on MMIF techniques is presented, along with medical imaging modalities, medical image fusion steps and levels, and the assessment methodology of MMIF. There are several image modalities, such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Single Photon Emission Computed Tomography (SPECT). Medical image fusion techniques are categorized into six main categories: spatial domain, transform fusion, fuzzy logic, morphological methods, and sparse representation methods. The MMIF levels are pixel-level, feature-level, and decision-level. The fusion quality evaluation metrics can be categorized as subjective/qualitative and objective/quantitative assessment methods. Furthermore, a detailed comparison between obtained results for significant MMIF techniques is also presented to highlight the pros and cons of each fusion technique.

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image.

Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients.

The power of transfer learning in agricultural applications: AgriNet.

Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we proposeAgriNetdataset, a collection of 160k agricultural images from more than 19 geographical locations, several images captioning devices, and more than 423 classes of plant species and diseases. We also introduceAgriNetmodels, a set of pretrained models on five ImageNet architectures: VGG16, VGG19, Inception-v3, InceptionResNet-v2, and Xception.AgriNet-VGG19 achieved the highest classification accuracy of 94% and the highest F1-score of 92%. Additionally, all proposed models were found to accurately classify the 423 classes of plant species, diseases, pests, and weeds with a minimum accuracy of 87% for the Inception-v3 model. Finally, experiments to evaluate of superiority ofAgriNetmodels compared to ImageNet models were conducted on two external datasets: pest and plant diseases dataset from Bangladesh and a plant diseases dataset from Kashmir.

BUS-Net: Breast Tumour Detection Network for Ultrasound Images Using Bi-directional ConvLSTM and Dense Residual Connections.

Breast ultrasound (BUS) imaging has become one of the key imaging modalities for medical image diagnosis and prognosis. However, the manual process of lesion delineation from ultrasound images can incur various challenges concerning variable shape, size, intensity, curvature, or other medical priors of the lesion in the image. Therefore, computer-aided diagnostic (CADx) techniques incorporating deep learning-based neural networks are automatically used to segment the lesion from BUS images. This paper proposes an encoder-decoder-based architecture to recognize and accurately segment the lesion from two-dimensional BUS images. The architecture is utilized with the residual connection in both encoder and decoder paths; bi-directional ConvLSTM (BConvLSTM) units in the decoder extract the minute and detailed region of interest (ROI) information. BConvLSTM units and residual blocks help the network weigh ROI information more than the similar background region. Two public BUS image datasets, one with 163 images and the other with 42 images, are used. The proposed model is trained with the augmented images (ten forms) of dataset one (with 163 images), and test results are produced on the second dataset and the testing set of the first dataset-the segmentation performance yielding comparable results with the state-of-the-art segmentation methodologies. Similarly, the visual results show that the proposed approach for BUS image segmentation can accurately identify lesion contours and can potentially be applied for similar and larger datasets.

Views on Deep Learning for Medical Image Diagnosis

Deep learning models are more often used in the medical field as a result of the rapid development of machine learning, graphics processing technologies, and accessibility of medical imaging data. The convolutional neural network (CNN)-based design, adopted by the medical imaging community to assist doctors in identifying the disease, has exacerbated this situation. This research uses a qualitative methodology. The information used in this study, which explores the ideas of deep learning and convolutional neural networks (CNN), taken from publications or papers on artificial intelligent (AI) Convolutional neural networks has been used in recent years for the analysis of medical image data. CNN's development of its machine learning roots is traced in this study. We also provide a brief mathematical description of CNN as well as the pre-processing process required for medical images before inserting them into CNN. Using CNN in many medical domains, including classification, segmentation, detection, and localization, we evaluate relevant research in the field of medical imaging analysis. It can be concluded that CNN's deep learning view of medical imaging is very helpful for medical parties in their work

Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network

ObjectiveThe traditional ICM is widely used in applications, such as image edge detection and image segmentation. However, several model parameters must be set, which tend to lead to reduced accuracy and increased cost. As medical images have more complex edges, contours and details, more suitable combinatorial algorithms are needed to handle the pathological diagnosis of multiple cerebral infarcts and acute strokes, resulting in the findings being more applicable, as well as having good clinical value. MethodsTo better solve the medical image fusion and diagnosis problems, this paper introduces the image fusion algorithm based on the combination of NSCT and improved ICM and proposes low-frequency, sub-band fusion rules and high-frequency sub-band fusion rules. The above method is applied to the fusion of CT/MRI images, subsequently, three other fusion algorithms, including NSCT-SF-PCNN, NSCT-SR-PCNN and Adaptive-PCNN are compared, and the simulation results of image fusion are analyzed and validated. ResultsAccording to the experimental findings, the suggested algorithm performs better than other fusion algorithms in terms of five objective evaluation metrics or subjective evaluation. The NSCT transform and the improved ICM were combined, and the outcomes were evaluated against those of other fusion algorithms. The CT/MRI medical images of healthy brain tissue, numerous cerebral infarcts and acute strokes were combined using this technique. ConclusionMedical image fusion using Adaptive-PCNN produces satisfactory results, not only in relation to improved image clarity but also in terms of outstanding edge information, high contrast and brightness.

Accurate classification of lung nodules on CT images using the TransUnet.

Computed tomography (CT) is an effective way to scan for lung cancer. The classification of lung nodules in CT screening is completely doctor dependent, which has drawbacks, including difficulty classifying tiny nodules, subjectivity, and high false-positive rates. In recent years, deep convolutional neural networks, a deep learning technology, have been shown to be effective in medical imaging diagnosis. Herein, we propose a deep convolutional neural network technique (TransUnet) to automatically classify lung nodules accurately. TransUnet consists of three parts: the transformer, the Unet, and global average pooling (GAP). The transformer encodes discriminative features via global self-attention modeling on CT image patches. The Unet, which collects context by constricting route, enables exact lunge nodule localization. The GAP categorizes CT images, assigning each sample a score. Python was employed to pre-process all CT images in the LIDI-IDRI, and the obtained 8,474 images (3,259 benign and 5,215 lung nodules) were used to evaluate the method's performance. The accuracies of TransUnet in the training and testing sets were 87.90 and 84.62%. The sensitivity, specificity, and AUC of the proposed TransUnet on the testing dataset were 70.92, 93.17, and 0.862%, respectively (0.844-0.879). We also compared TransUnet to three well-known methods, which outperformed these methods. The experimental results on LIDI-IDRI demonstrated that the proposed TransUnet has a great performance in classifying lung nodules and has a great potential application in diagnosing lung cancer.

Revisiting PFA-mediated tissue fixation chemistry: FixEL enables trapping of small molecules in the brain to visualize their distribution changes

Various small molecules have been used as functional probes for tissue imaging in medical diagnosis and pharmaceutical drugs for disease treatment. The spatial distribution, target selectivity, and diffusion/excretion kinetics of small molecules in structurally complicated specimens are critical for function. However, robust methods for precisely evaluating these parameters in the brain have been limited. Herein, we report a new method termed "fixation-driven chemical cross-linking of exogenous ligands (FixEL)," which traps and images exogenously administered molecules of interest (MOIs) in complex tissues. This method relies on protein-MOI interactions and chemical cross-linking of amine-tethered MOI with paraformaldehyde used for perfusion fixation. FixEL is used to obtain images of the distribution of the small molecules, which addresses selective/nonselective binding to proteins, time-dependent localization changes, and diffusion/retention kinetics of MOIs such as the scaffold of PET tracer derivatives or drug-like small molecules.

Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network

The effect of segmentation on lung X-ray image classification has been analyzed in this study. The 150 lung x-ray images in this study were separated into 78 as training data, 30 as validation data, and 42 as testing in three categories: normal lungs, effusion lungs, and cancer lungs. In pre-processing, the images were modified by adaptive histogram equalization to improve image quality and increase image contrast. The segmentation aims to mark the image by contouring the lung area obtained from the thresholding and some morphological manipulation processes such as filling holes, area openings, and labelling. Image classification uses Convolutional Neural Network (CNN) with five convolution layers, an Adam optimizer, and 30 epochs. The segmentation effect is analyzed by comparing the classification performance of the segmented and unsegmented images. In the study, the unsegmented X-ray image dataset classification reached an overall accuracy of 59.52% in the network testing process. The segmented X-ray image dataset obtained greater accuracy, 73.81%. It indicated that the segmentation process could improve network performance because the input pattern of the segmented image is easier to classify. Furthermore, the segmentation technique in the study can be one of the alternatives to developing image classification technologies, especially for medical image diagnosis. Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network.

A Federated Learning Framework for Breast Cancer Histopathological Image Classification

Quantities and diversities of datasets are vital to model training in a variety of medical image diagnosis applications. However, there are the following problems in real scenes: the required data may not be available in a single institution due to the number of patients or the type of pathology, and it is often not feasible to share patient data due to medical data privacy regulations. This means keeping private data safe is required and has become an obstacle in fusing data from multi-party to train a medical model. To solve the problems, we propose a federated learning framework, which allows knowledge fusion achieved by sharing the model parameters of each client through federated training rather than sharing data. Based on breast cancer histopathological dataset (BreakHis), our federated learning experiments achieve the expected results which are similar to the performances of the centralized learning and verify the feasibility and efficiency of the proposed framework.

Automated meniscus segmentation and tear detection of knee MRI with a 3D mask-RCNN

BackgroundThe diagnostic results of magnetic resonance imaging (MRI) are essential references for arthroscopy as an invasive procedure. A deviation between medical imaging diagnosis and arthroscopy results may cause irreversible damage to patients and lead to excessive medical treatment. To improve the accurate diagnosis of meniscus injury, it is urgent to develop auxiliary diagnosis algorithms to improve the accuracy of radiological diagnosis.PurposeThis study aims to present a fully automatic 3D deep convolutional neural network (DCNN) for meniscus segmentation and detects arthroscopically proven meniscus tears.Materials and methodsOur institution retrospectively included 533 patients with 546 knees who underwent knee magnetic resonance imaging (MRI) and knee arthroscopy. Sagittal proton density-weighted (PDW) images in MRI of 382 knees were regarded as a training set to train our 3D-Mask RCNN. The remaining data from 164 knees were used to validate the trained network as a test set. The masks were hand-drawn by an experienced radiologist, and the reference standard is arthroscopic surgical reports. The performance statistics included Dice accuracy, sensitivity, specificity, FROC, receiver operating characteristic (ROC) curve analysis, and bootstrap test statistics. The segmentation performance was compared with a 3D-Unet, and the detection performance was compared with radiological evaluation by two experienced musculoskeletal radiologists without knowledge of the arthroscopic surgical diagnosis.ResultsOur model produced strong Dice coefficients for sagittal PDW of 0.924, 0.95 sensitivity with 0.823 FPs/knee. 3D-Unet produced a Dice coefficient for sagittal PDW of 0.891, 0.95 sensitivity with 1.355 FPs/knee. The difference in the areas under 3D-Mask-RCNN FROC and 3D-Unet FROC was statistically significant (p = 0.0011) by bootstrap test. Our model detection performance achieved an area under the curve (AUC) value, accuracy, and sensitivity of 0.907, 0.924, 0.941, and 0.785, respectively. Based on the radiological evaluations, the AUC value, accuracy, sensitivity, and specificity were 0.834, 0.835, 0.889, and 0.754, respectively. The difference in the areas between 3D-Mask-RCNN ROC and radiological evaluation ROC was statistically significant (p = 0.0009) by bootstrap test. 3D Mask RCNN significantly outperformed the 3D-Unet and radiological evaluation demonstrated by these results.Conclusions3D-Mask RCNN has demonstrated efficacy and precision for meniscus segmentation and tear detection in knee MRI, which can assist radiologists in improving the accuracy and efficiency of diagnosis. It can also provide effective diagnostic indicators for orthopedic surgeons before arthroscopic surgery and further promote precise treatment.

Modification of [18F]-FDG PET/CT imaging protocols in obese oncology patients: A nationwide survey

IntroductionThe use of medical imaging for diagnosis, staging and follow-up in Oncology context is incredibly important, being the use of [18F]-FDG PET/CT particularly advantageous in specific contexts like the case of obese patients. However, imaging the latter can be challenging sometimes, since their own body size may affect overall image quality and adds technical difficulties for the operator(s) performing the examination. MethodsThis research project was developed with the aim of analysing the current personal practices of Portuguese Nuclear Medicine Technologists (NMTs) in the adaptation of 18F-FDG PET/CT oncological protocols for obese patients and comparing the results with parameters referenced in literature. A non-experimental research study was conducted using a survey delivered online to NMTs through social media platforms (Facebook® and LinkedIn®) and by sending the link directly to contacts within the research team professional and personal networks. ResultsAnswers from a total of 26 participants were obtained, with 88.5% of participants admitting modifying technical protocols in examinations for obese patients. Changes in PET protocols included an increase in the administered activity (60.9%), an increase in scan time per individual bed position (69.6%) and the use of Time-of-Flight (TOF) technology whenever available. Protocol changes in CT included increasing the mA (82.6%), raising the KVp (47.8%), the application of iterative reconstruction (69.6%) and the use of automatic exposure control (AEC) (52.2%). The remaining parameters (pitch, algorithm, slice thickness, display FOV, gantry rotation time and energy acceptance window) were claimed not to be modified by around 90% of professionals. ConclusionPortuguese NMTs tend to change the [18F]-FDG PET/CT protocols for obese patients. However, while some of the parameters appear to be contradictory or redundant, others require further optimisation, especially in the CT component. Implications for practiceEfforts should be made to optimize acquisition protocols used in [18F]-FDG PET/CT scans for obese patients.

Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis.

During clinical practice, radiologists often use attributes, e.g., morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning and a graph convolutional network branch performing more generic relational modeling and reasoning using a feature representation. Tight coupling between these two branches is achieved via a cross-network attention mechanism and the fusion of their classification results. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks. On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95.36% and an AUC of 96.54%. Our method also achieves a 3.24% accuracy improvement on an in-house chest X-ray image dataset for tuberculosis diagnosis. Our ablation study indicates that our hybrid algorithm achieves a much better generalization performance than a pure neural network architecture under very limited training data.