Volumetric Feature Learning for Query-by-Example in Medical Imaging Archives

Pinho, Eduardo; Silva, João Figueira; Costa, Carlos

doi:10.1109/cbms.2019.00038

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Accurately, a transducer, compare performance to receive PA signals using three different central frequencies, which include different target spectral information from the surrounding circuit and train the network to the end [19]. Current research, textbased, visual systems, or through method blending, while solving medical image retrieval expression learning by solving the problem of encoding medical image content to become a compact representation problem, which can play an essential role in improving the search function effect [20].…”

Section: Related Workmentioning

confidence: 99%

Detection of Leukemia in Microscopic White Blood Cell Images Using Gaussian Feature Convolutional Visual Recognition Algorithm

2020

jcr

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Cancer detection has always been a challenge in the diagnosis and treatment plan for hematological diseases. Manual identification of cancers from microscopic images is necessary. It depends on other factors, including the lack of microscopic images as their knowledge and precise measurement of specific usual or cancer expert classifications. Automated identification of cancer cells from microscopic section images and helps to improve the problem mentioned above, based on biological interpretation. Detection of blood cancer cells by a patient's blood mark microscopic examination using different techniques of image processing. White blood cell cancer images and their essential three-stage there are an enhancement, segmentation, and classification. The stage of image processing to achieve more quality and accuracy in detecting blood cancers. Approaching the conventional segmentation and counting of blood cells is considered as an essential step that helps to extract features to diagnose diseases like leukemia. The image analysis will allow hematologist experts to perform faster and more accurately. In this work, the image recognition problem of White Blood Cells (WBC) cancer is investigated. Different types of white blood cells are classified using a Gaussian Feature Convolutional Visual Recognition (GFCVR).The most important features or segments of these blood cells are provided as input microscopic images to the neural network. The consequences of this analysis are mainly considering the identification of blood cancer affected region with feature extraction for specific white blood cell images. In this proposed approach pre-processing the image, classifies multi-level clustering to find the affected area in blood cells. In this proposed simulation, results in time complexity, accuracy, recall, perception, false classification ratio gives better results compare with other existing methods.

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Section: Related Workmentioning

confidence: 99%

Detection of Leukemia in Microscopic White Blood Cell Images Using Gaussian Feature Convolutional Visual Recognition Algorithm

2020

jcr

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“…The development of techniques to measure image similarity by capturing the unstructured features of clinical data has been the focus of the Content-Based Medical Image Retrieval (CBMIR) challenge, which has previously been thought of as a computer science issue. In a query-by-example approach [4] CBMIR systems frequently use an example image as the query image in a query-by-example technique and extract a matching query vector that encapsulates the needed information. The reference images with the query vector's most comparable reference vectors are found in a database during the retrieval process.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

Iqbal,

Qureshi,

Alhussein

et al. 2023

IEEE Access

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Medical image retrieval is essential to modern medical treatment because it enables doctors to diagnose and treat a variety of illnesses. In this study, we present an innovative technique for selecting the methodology of medical images by combining textural and visual information. Knowing the imaging process behind an idea, such as a chest X-ray, skin dermatology, or breast histopathology image, may be extremely helpful to healthcare professionals since it can aid in image investigation and provide important information about the imaging technique used. We use deep learning-based feature engineering to do this, using both the textural and visual components of healthcare images. We extract detailed visual information from the images using a predefined Convolutional Neural Network (CNN). The Global-Local Pyramid Pattern (GLPP), Zernike moments, and Haralick are also used to physically separate the pertinent parts from the images' other visual and factual aspects. These essential characteristics, such as image modality and imaging technique-specific characteristics, provide additional information about the technology. We employ a feature fusion method that incorporates the depictions obtained from the two modalities in order to combine the textural and visual elements. This fusion process, which improves the discrimination capacity of the feature vectors, makes accurate modality classification possible. We conducted trials on a sizable dataset consisting of various medical images to assess the effectiveness of our proposed method. The results indicate that, in comparison to conventional methods, our technique outperforms modality retrieval, with a precision of 95.89 and a recall of 96.31. The accuracy and robustness of the classification task are greatly creased by the combination of textural and visual data. Through the integration of textural and visual information, our work offers a unique method for recovering the modality of medical images. This method has the potential to greatly improve the speed and accuracy of medical image processing and diagnosis by helping experts rapidly and accurately identify the imaging technology being utilized.

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Volumetric Feature Learning for Query-by-Example in Medical Imaging Archives

Cited by 2 publications

References 18 publications

Detection of Leukemia in Microscopic White Blood Cell Images Using Gaussian Feature Convolutional Visual Recognition Algorithm

Detection of Leukemia in Microscopic White Blood Cell Images Using Gaussian Feature Convolutional Visual Recognition Algorithm

Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

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