Effective Utilization of Image Information Using Data Mining Technique

Saravanan, D.; Joseph, Dennis; Vaithyasubramanian, S.

doi:10.1007/978-3-030-32644-9_22

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…The algorithms needed to perform the mining of associations within the context of image [12] [13]. The major steps involved for this procedure is :…”

Section: Image Mining Algorithm Stepsmentioning

confidence: 99%

A Study on Content based Image Retrieval Using Knowledge Extraction Technique

2024

Journal of Informatics Education and Research

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Among the available digital datasets, multimedia data differs from traditional data repositories. The creation of this data type is easy for the user due to the technological support. Even untrained users can create and store this content in a multimedia data repository. Because of the need for specific data extraction, this field is currently in high demand. Many existing procedures lack support for extracting similar content from these datasets. This serves as the motivation for conducting this study. Immediate attention is necessary for storing and generating key terms for these datasets. The perseverance of this paper is to discover several techniques of information extraction, present their findings, and propose a new approach to multimedia information retrieval.

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“…The algorithms needed to perform the mining of associations within the context of image [12] [13]. The major steps involved for this procedure is :…”

Section: Image Mining Algorithm Stepsmentioning

confidence: 99%

A Study on Content based Image Retrieval Using Knowledge Extraction Technique

2024

Journal of Informatics Education and Research

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“…A convolutional neural network (or CNN) is a special type of multilayer neural network or deep learning architecture inspired by the visual system of living things [7]. A convolutional neural network (CNN) is a special type of neural network for processing data that has a grid-like topology [8].…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

Bulldog Breed Classification Using VGG-19 and Ensemble Learning

Jinan¹,

Situmorang²,

Rosnelly³

2023

ICoSTEC

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In image classification, the C4.5, Adaboost, and Gradient Boosting algorithms need another method to extract the image's features in the classification process. This research employs transfer learning with the VGG-19 network for the image's features extraction and transfers the result as a dataset to classify image-based Bulldog breeds. As the classifier to classify the extracted features from the VGG 16 model, we employ three ensemble learning algorithms, namely C4.5, AdaBoost, and Gradient Boost. The training data classification results of the American, English, and French bulldog breeds show that, with a 20-fold cross-validation evaluation, the Gradient Boosting algorithm performs the best, with an accuracy value of 0.958, a precision value of 0.958 and recall value of 0.933. And show the highest accuracy (0.933), precision (0.938), and recall (0.933) in the testing data classification. While in the testing data classification, the Gradient Boosting algorithm scores an accuracy value of 0.933, a precision value of 0.938, and a recall value of 0.933.

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“…However, in females, breast cancer is mostly the leading cause of mortality [4][5][6]. More than 8% of women will endure this infection during their lifetime [7]. Breast cancer is a malignant tumor that is J of Biomedical Photonics & Eng 7 (2) 6 May 2021 © J-BPE 020302-2 established from cells in the breast with no symptoms in the early stages [8].…”

Section: Introductionmentioning

confidence: 99%

“…To decrease the quantity of tumor-positive resection edges, different methods for resection edge appraisal during the breast-conserving medical procedure have been proposed [19][20][21]. Currently, the available edge assessment methods are frozen section analysis, imprint cytology, specimen radiography, and ultrasound [7,[21][22][23]. Despite this, none of all these techniques could clinically extensive.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Normal and Malignant Breast Tissues utilizing Hyperspectral Images and Associated Differential Spectrum Algorithm

Aref

Youssef

El-Sharkawy

2021

J-BPE

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Breast malignancy is the most pervasive disease and a significant reason for death in women around the world. Recently, Photonic technologies play a vital role in medical applications. This study presents an outline of recent outcomes on the magnitude of breast tissue optical properties. We established an optical system setup utilizing a hyperspectral (HS) camera with poly-chromatic source lights with wavelength (380~1050 nm) for this investigation. Measuring the diffuse reflection (Ŗd) of the investigated ex vivo breast sample to select the optimum spectral image to differentiate between the normal and tumor in the near infra-red and visible (NIR–VIS) spectrum. Finally, applying the custom algorithm to increase the image contrast and applying contour delineation of the malignant regions. The experimental analysis indicates key spectroscopic variations between normal tissue and malignant region in range (550~650 nm). Although, after data normalization, there was noticeable variation at three ranges (630–680 nm), (720–770 nm), and (830–880 nm). The calculated standard deviation (Şd) between the normal and cancer tissue to validate the selective ranges shows that the highest contrast at wavelength 680 nm. However, the histogram analysis illustrates that the spectral image at 600 nm was higher contrast and wavelength 400 nm was the lowest contrast from the select seven-spectral images (400, 500, 600, 700, 800, 900, 1000 nm) to avoid the processing time of the captured HS 128-frames. The proposed potential method could provide promising results on the investigated breast sample optical properties in the diagnostic applications to assist the pathologist and the surgeon. Where the optimum wavelength at 680 nm for diagnostic applications and the ideal spectral image at 600 nm discriminate between the normal and malignant tissue.

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Effective Utilization of Image Information Using Data Mining Technique

Cited by 5 publications

References 11 publications

A Study on Content based Image Retrieval Using Knowledge Extraction Technique

A Study on Content based Image Retrieval Using Knowledge Extraction Technique

Bulldog Breed Classification Using VGG-19 and Ensemble Learning

Characterization of Normal and Malignant Breast Tissues utilizing Hyperspectral Images and Associated Differential Spectrum Algorithm

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