An Effective Deep Learning Model for Content-Based Gastric Image Retrieval

Content Based Image Retrieval (CBIR) is a process in which for a given query image similar images will be retrieved based on the image content similarity. Image content refers to its visual features, which are mathematical representations of a digital image. The image retrieval task primarily depends on image feature extraction and similarity measurement between the feature vectors. The performance of CBIR process not only decided by the optimum features extracted from the image but also on the proper choice of the similarity/dissimilarity measures (distance metrics). As the image features broadly diversified in terms of color, texture and shape based, using the same distance metric may not work well for all of these features. In this paper, first we presented overview of geometric and statistical distance metrics used in CBIR along with the comparative analysis of these measures on color and texture features. Color features extracted by computing color histograms in HSV space and texture features by wavelet decompositions. Geometrical distances such as Manhattan, Chebyshev, Euclidean, statistical distance metrics such as Cosine Similarity, Chi-square, KullbackLeibler, Jeffrey and cumulative statistical distance metrics of Kolmogorov-Smirnov, Cramer von Mises and Earthmover's distances were analyzed for feature similarity. We gave certain conclusions on the performance of all these distance metrics in terms of Mean Average Precision (MAP) and Recall rates with color and texture features respectively.

BackgroundReducing TB surveillance incurs significant costs, as effective control and societal impact minimization rely on robust, ongoing surveillance systems that integrate and analyze comprehensive data for prompt action. Our study supports integrating artificial intelligence (LSI) into TB and other notifiable disease monitoring frameworks to improve trackingFigure 1Binary Matrix of 125 Terms: Presence vs. Absence of Root Keywords Retrieved from the Patient ElectronicRecord for the LSI Automatic Retrieval System.Binary Matrix of 125 Terms: Presence vs. Absence of Root Keywords Retrieved from the Patient Electronic Record for the LSI Automatic Retrieval System.MethodsIn 2024, we developed a novel application within Datamart A.R.G.U.S.—Assistant for Recovery and Guarding of Urgent and Epidemiological Sentinels—a cloud-based platform hosted on AWS and developed by our team. This application integrates with hospital systems (MV) and laboratory systems (MATRIX and GAL) to enhance the surveillance of healthcare-associated infections (HAI) and notifiable diseases. Data from both hospital and outpatient care are encoded into a binary matrix of 125 terms (root keywords) extracted from patient electronic records. This matrix is then processed using Singular Value Decomposition (SVD) within an LSI-based information retrieval system (Fig. 1). In our model, each patient is represented as a point in a 125-dimensional hyperdimensional space. When projected into three-dimensional space, this framework allows for effective clustering and classification of notifiable Diseases (Fig. 2), specifically TB (Fig. 3).Figure 2:3D Visualization of High-Dimensional Data: Comparing Mandatory Notifiable Diseases versus Non-Notifiable Diseases. Both patient types are well separated in the space, enabling automatic clustering and classification.3D Visualization of High-Dimensional Data: Comparing Mandatory Notifiable Diseases versus Non-Notifiable Diseases. Both patient types are well separated in the space, enabling automatic clustering and classification.ResultsThe LSI-based matrix was constructed using data from January 2023 to February 2024, encompassing a sample of 17,895 cases (lines) across 125 presence versus absence terms, with each case classified as either a notifiable or non-notifiable disease. The 125-dimensional matrix was reduced to 28 factors using SVD. The sensitivity and specificity of the Euclidean distance were better than those of cosine similarity when automatically identifying TB cases in 1,970 independent queries (Fig. 4).Figure 3:3D Visualization of High-Dimensional Data: Comparing Tuberculosis versus Other Mandatory Notifiable Diseases versus Non-Notifiable Diseases. The three types of patients are well separated in the space, enabling automatic clustering and classification.3D Visualization of High-Dimensional Data: Comparing Tuberculosis versus Other Mandatory Notifiable Diseases versus Non-Notifiable Diseases. The three types of patients are well separated in the space, enabling automatic clustering and classification.ConclusionArtificial Intelligence (LSI), significantly enhances the surveillance of TB and other notifiable diseases by enabling real-time, automated case identification up to one day prior (D-1). This advancement allows surveillance personnel to complete their tasks more efficiently, thereby preventing outbreaks and reducing the risk of unforeseen fatalities.Figure 4:Performance Comparison of Cosine vs. Euclidean Distance Metrics in Automatically Identifying TB Cases Using LSI: Euclidean Distance Demonstrates Superior Accuracy and Has Been Integrated into Datamart A.R.G.U.S. for Real-Time Identification of TB and Other Notifiable Diseases as of the Previous Workday (D-1).Performance Comparison of Cosine vs. Euclidean Distance Metrics in Automatically Identifying TB Cases Using LSI: Euclidean Distance Demonstrates Superior Accuracy and Has Been Integrated into Datamart A.R.G.U.S. for Real-Time Identification of TB and Other Notifiable Diseases as of the Previous Workday (D-1).DisclosuresAll Authors: No reported disclosures

The volume of data has increased fast, particularly medical images, in big hospitals over the last years. This increase imposes a big challenge to medical specialists: the maintenance of high interpretation accuracy of image-based diagnosis. Computer-Aided Diagnosis software allied to the Content-Based Image Retrieval (CBIR) can provide decision support to specialists by allowing them to find images from a database that are similar to a reference image. However, a well known challenge of CBIR is the processing time that it takes to process all comparisons between the reference image and the image database. This paper proposes a performance evaluation of medical Image Similarity Analysis (ISA) in a heterogeneous single-, multi- and many-core architecture using the high performance parallel OpenCL framework. A CBIR algorithm was implemented to validate the proposal. The algorithm used a Lung Cancer image database with 131, 072 Computed Tomography scans, Texture Attributes for image features and Euclidean Distance for image comparison metrics. The results showed that the OpenCL parallelism can increase the performance of ISA, especially using the GPU, with speedups of 3x, 36x and 64x. The results also showed that it is not worth the use of GPU local memory for the Euclidean Distance metrics due to its low performance improvement and high implementation complexity in comparison to the GPU global memory. That being said, GPU is a safer medical CBIR approach than further distributed environments as clusters, cloud and grid computing because GPU usage does not require the patient data to be transfered to other machines.

The optimization of an assembly line layout can be modeled as a facility layout problem. Since an accurate representation of the real-world distances is essential for the validity of the optimization results, this paper compares the accuracy of the approximation of the real-world distances by the commonly used rectilinear and Euclidean distance metrics. Further, the influence of the size and aspect ratio of the machines on the approximation accuracy and the influence of the distance metrics on the solver runtime is studied. The results show the Euclidean distance metric being more accurate but extending computation time.

Robust microarray data feature selection using a correntropy based distance metric learning approach

Decision letter for "‘Euclimatch': an R package for climate matching with Euclidean distance metrics"

Decision letter for "‘Euclimatch': an R package for climate matching with Euclidean distance metrics"

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While the Euclidean distance metric is commonly utilized in most soft subspace clustering algorithms, some robust distance metrics can also be considered for soft subspace clustering. In this study, a novel soft subspace clustering algorithm called Alternative Soft Subspace Clustering (ASSC) is proposed by incorporating the alternative distance metric into the framework of entropy weighting subspace clustering. The properties of ASSC are investigated and a theoretical analysis on robustness is established accordingly. Experiments on UCI data are conducted and the experimental results show the effectiveness of the proposed algorithm.

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.

Clustering methods are commonly used for fMRI (functional Magnetic Resonance Imaging) data analysis. Based on an effective clustering algorithm called Affinity Propagation (AP) and a new defined similarity measure, we present a method for detecting activated brain regions. In the proposed method, autocovariance function values and the Euclidean distance metric of time series are firstly calculated and combined into a new similarity measure, then the AP algorithm with the measure is carried out on all time series of data, and at last regions with which their cross-correlation coefficients are greater than a threshold are taken as activations. Without setting the number of clusters in advance, our method is especially appropriate for the analysis of fMRI data collected with a periodic experimental paradigm. The validity of the proposed method is illustrated by experiments on a simulated dataset and a benchmark dataset. It can detect all activated regions in the simulated dataset accurately, and its error rate is smaller than that of K-means. On the benchmark dataset, the result is very similar to SPM.

Sparse Locally Adaptive Regression Kernel For Face Verification

In the current years, advancements in medical imaging have prompted the rise of some massive databases, including the pictures from a different scope of modalities. Content based image retrieval (CBIR) is a kind of search that selects examples from an image set that have a similar content to an input query image. Each CBIR system consists of two substantial factors, feature extraction and similarity measure. Based on the type of these parts, many CBIR systems are proposed. In this paper we present not only how to design a CBIR system for MRI brain Tumor images, but also a comparison between the different implemented CBIR systems and ours. Furthermore, in this research, using a DML (Distance Metric Learning) instead of the rigid distance metric such as Euclidean distance as the Similarity measure has helped the researchers to obtain a better mAP (mean Average Precision) as the result. The experimental results of our CBIR system show that it has a better performance than other rivals in the field. The obtained mean average precision of our CBIR system is 92.41 that is significant in MRI brain tumor.

The accuracy of frequency prediction is markedly affected by the nonlinearity and model degradation caused by multiple environmental conditions, which may hinder the prediction accuracy and detectability of damage. Therefore, this paper proposes a local correlation model (LCM) between multiorder bridge frequencies and multiple environmental factors for early warning of abnormal frequencies. First, partial least-squares analysis was conducted to extract several environmental principal components sensitive to modal frequencies. The most relevant local data set for each online environmental sample was selected according to similarity measurements based on Euclidean distance metrics. On this basis, more accurate environment–frequency relation models were formed using relatively simple local linear regression models. To filter out the residual environmental variability not suppressed by the LCM and to enlarge slightly abnormal frequency variation, a warning index (i.e., the weighted Mahalanobis distance) was defined using the residual subspatial reconstruction of principal component analysis. Finally, the validity of the proposed method was verified on a cable-stayed bridge. The results show that in contrast to conventional methods, the proposed LCM can accurately describe complicated frequency variations under changing environmental conditions by considering both the nonlinearity of environmental conditions and the time-varying properties of relation models. The detectability of frequency anomalies induced by sudden events can be effectively improved.