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BACKGROUND: Content-based image retrieval (CBIR) systems are vital for managing the large volumes of data produced by medical imaging technologies. They enable efficient retrieval of relevant medical images from extensive databases, supporting clinical diagnosis, treatment planning, and medical research. OBJECTIVE: This study aims to enhance CBIR systems' effectiveness in medical image analysis by introducing the VisualSift Ensembling Integration with Attention Mechanisms (VEIAM). VEIAM seeks to improve diagnostic accuracy and retrieval efficiency by integrating robust feature extraction with dynamic attention mechanisms. METHODS: VEIAM combines Scale-Invariant Feature Transform (SIFT) with selective attention mechanisms to emphasize crucial regions within medical images dynamically. Implemented in Python, the model integrates seamlessly into existing medical image analysis workflows, providing a robust and accessible tool for clinicians and researchers. RESULTS: The proposed VEIAM model demonstrated an impressive accuracy of 97.34% in classifying and retrieving medical images. This performance indicates VEIAM's capability to discern subtle patterns and textures critical for accurate diagnostics. CONCLUSIONS: By merging SIFT-based feature extraction with attention processes, VEIAM offers a discriminatively powerful approach to medical image analysis. Its high accuracy and efficiency in retrieving relevant medical images make it a promising tool for enhancing diagnostic processes and supporting medical research in CBIR systems.

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In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches.

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Digital well-being records are multimodal and high-dimensional (HD). Better theradiagnostics stem from new computationally thorough and edgy technologies, i.e., hyperspectral (HSI) imaging, super-resolution, and nanoimaging, but advance mess data portrayal and retrieval. A patient's state involves multiple signals, medical imaging (MI) modalities, clinical variables, dialogs between clinicians and patients, metadata, genome sequencing, and signals from wearables. Patients' high volume, personalized data amassed over time have advanced artificial intelligence (AI) models for higherprecision inferences, prognosis, and tracking. AI promises are undeniable, but with slow spreading and adoption, given partly unstable AI model performance after real-world use. The HD data is a ratelimiting factor for AI algorithms generalizing real-world scenarios. This paper studies many health data challenges to robust AI models' growth, aka the dimensionality curse (DC). This paper overviews DC in the MIs' context, tackles the negative out-of-sample influence and stresses important worries for algorithm designers. It is tricky to choose an AI platform and analyze hardships. Automating complex tasks requires more examination. Not all MI problems need automation via DL. AI developers spend most time refining algorithms, and quality data are crucial. Noisy and incomplete data limits AI, requiring time to handle control, integration, and analyses. AI demands data mixing skills absent in regular systems, requiring hardware/software speed and flexible storage. A partner or service can fulfill anomaly detection, predictive analysis, and ensemble modeling.

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Graphical Search Engines are conceptually used in many development areas surrounding information retrieval systems that aim to provide a visual representation of results, typically associated with retrieving images relevant to one or more input images. Since the 1990s, efforts have been made to improve the result quality, be it through improved processing speeds or more efficient graphical processing techniques that generate accurate representations of images for comparison. While many systems achieve timely results by combining high-level features, they still struggle when dealing with large datasets and abstract images. Image datasets regarding industrial property are an example of an hurdle for typical image retrieval systems where the dimensions and characteristics of images make adequate comparison a difficult task. In this paper, we introduce an image retrieval system based on a multi-phase implementation of different deep learning and image processing techniques, designed to deliver highly accurate results regardless of dataset complexity and size. The proposed approach uses image signatures to provide a near exact representation of an image, with abstraction levels that allow the comparison with other signatures as a means to achieve a fully capable image comparison process. To overcome performance disadvantages related to multiple image searches due to the high complexity of image signatures, the proposed system incorporates a parallel processing block responsible for dealing with multi-image search scenarios. The system achieves the image retrieval through the use of a new similarity compound formula that accounts for all components of an image signature. The results shows that the developed approach performs image retrieval with high accuracy, showing that combining multiple image assets allows for more accurate comparisons across a broad spectrum of image typologies. The use of deep convolutional networks for feature extraction as a means of semantically describing more commonly encountered objects allows for the system to perform research with a degree of abstraction.

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Todays, COVID-19 has caused much death and its spreading speed is increasing, regarding virus mutation. This outbreak warns diagnosing infected people is an important issue. So, in this research, a computer-aided diagnosis (CAD) system called COV-CAD is proposed for diagnosing COVID-19 disease. This COV-CAD system is created by a feature extractor, a classification method, and a content-based imaged retrieval (CBIR) system. The proposed feature extractor is created by using the modified AlexNet CNN. The first modification changes ReLU activation functions to LeakyReLU for increasing efficiency. The second change is converting a fully connected (FC) layer of AlexNet CNN with a new FC, which results in reducing learnable parameters and training time. Another FC layer with dimensions 1 × 64 is added at the end of the feature extractor as the feature vector. In the classification section, a new classification method is defined in which the majority voting technique is applied on outputs of CBIR, SVM, KNN, and Random Forest for final diagnosing. Furthermore, in retrieval section, the proposed method uses CBIR because of its ability to retrieve the most similar images to the image of a patient. Since this feature helps physicians to find the most similar cases, they could conduct further statistical evaluations on profiles of similar patients. The system has been evaluated by accuracy, sensitivity, specificity, F1-score, and mean average precision and its accuracy for CT and X-ray datasets is 93.20% and 99.38%, respectively. The results demonstrate that the proposed method is more efficient than other similar studies.

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A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database.

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Pattern recognition and feature extraction of images always have been important subjects in improving the performance of image classification and Content-Based Image Retrieval (CBIR). Recently, Machine Learning and Deep Learning algorithms are utilized widely in order to achieve these targets. In this research, an efficient method for image description is proposed which is developed by Machine Learning and Deep Learning algorithms. This method is created using combination of an improved AlexNet Convolutional Neural Network (CNN), Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) descriptors. Furthermore, the Principle Component Analysis (PCA) algorithm has been used for dimension reduction. The experimental results demonstrate the superiority of the offered method compared to existing methods by improving the accuracy, mean Average Precision (mAP) and decreasing the complex computation. The experiments have been run on Corel-1000, OT and FP datasets.

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In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of average recall and average precision values and compared with six similar methods for biomedical image classification. The average precision obtained for the proposed system is found to be 95.26% and the average recall value is found to be 69.56% in average for the two databases.

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Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance. In response, we exploit joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features. Supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress multiple high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on 3121 breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.3% classification accuracy within 16.5 ms query time, comparing favorably with traditional methods.

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Se presenta el proceso de caracterización implementado para la obtención de descriptores visuales que representan el contenido visual de imágenes digitales de biopsias de cuello uterino infectadas con el Virus del Papiloma Humano (VPH), en las que se capturan tejidos con lesiones conocidas como Condiloma Plano Viral. A partir de la construcción de una base de datos de imágenes de biopsias de cuello uterino y el análisis e implementación de técnicas de filtrado que resaltan la información relacionada a las texturas contenidas en los tejidos que captura cada imagen y de técnicas de extracción de características que describen el contenido de las imágenes; se propone un conjunto de características que describen el contenido de las imágenes a partir de modificaciones propias de la Transformada Discreta de Wavelets y el cálculo de la Matriz de Coocurrencia, donde este conjunto de características propuesto proporcionó un porcentaje promedio de recuperación del 80% en imágenes microscópicas de cuello uterino infectadas con el VPH, sobre las cuales no se conocen sistemas CBIR desarrollados. Finalmente, se determina el porcentaje de recuperación promedio a partir del uso de métricas de similaridad basadas en la norma LP.

The purpose of this work is to report the characterization process implemented to obtain visual descriptors representing visual content of digital images of cervical biopsies infected with Human Papilloma Virus (HPV). Positive biopsies with infected tissues present lesions known as Condyloma Plano Viral. A database of images of cervical biopsies was constructed in addition to the implementation of techniques that enhance the texture information and describe the content of images. This work proposed a set of features to describe the content of images from custom modifications of Discrete Wavelet Transform and the calculation of the Co-occurrence Matrix. This proposed feature set provided an average recovery rate of 80% in microscopic images of the cervix infected with HPV, from which CBIR systems have not been developed. Finally, this work determines the average recovery rate from the use of similarity metrics based on the standard LP.

Neste trabalho é apresentado o processo implementado de caracterização para a obtenção de descrições visuais que representam o conteúdo visual de imagens digitais de biópsias cervicais infectadas com Papilomavírus Humano (HPV), capturadas em lesões de tecidos conhecidas como Condiloma Plano Viral. A partir da construção de uma base de dados de imagens de biópsias do colo uterino, análise e implementação de técnicas de filtragem de características que descrevem o conteúdo das imagems, propõe-se um conjunto de características que descrevem o conteúdo das imagens a partir de modificações próprias da Transformada Discreta de Wavelets e o cálculo da Matriz de co-ocorrência, onde o conjunto de características propostas resultou numa porcentagem média de 80% de recuperação nas imagens microscópicas de colo uterino infectado com o VPH, sobre as quais não se percebe o desenvolvimento dos sistemas CBIR. Finalmente, a taxa de recuperação média foi determinada a partir da utilização de métricas de similaridade com base na indicação de LP.