Calculation of Filtration Characteristics of Porous Media by Their Digitized Images with the Use of Lattice Boltzmann Equations

Zakirov, T. R.; Statsenko, Evgeny; Хайдарова, Л. І.

doi:10.1007/s10891-018-1833-9

Cited by 18 publications

(2 citation statements)

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“…The most recycled processes to diagnose breast cancer are mammography, biopsy, thermography and ultrasound 7,8 . A biopsy is standard clinic methodology utilized to diagnose early‐stage cancer on microscope 9,10 . Based on subjective nature of human clarification, radiologists can have dissimilar opinions about related mammograms 7 .…”

Section: Introductionmentioning

confidence: 99%

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A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

et al. 2022

Concurrency and Computation

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In this manuscript, a Wrapper based feature extraction framework based on AlexNet deep convolutional neural network (ADCNN) with gradient‐based optimizer (GBO) is proposed for early detection of breast cancer. In this input images are occupied as mini‐mammography image analysis society (MIAS) database. Then the images are pre‐processed to eliminate that noises using Markov random field (MRF) method. Image features are extracted by the process of Wrapper based feature extraction framework with ADCNN. Then, the weight parameters of ADCNN are optimizing through the aid of GBO. Then the mammogram images are characterized as normal or abnormal (malignant and benign) with SVM classifier. The simulation process is implemented on MATLAB platform. The proposed ADCNN‐SVM‐GBO attains higher accuracy 34.64%, 28.86%, 19.86%, 24.64%, 32.86%, higher Precision 28.07%, 18.96%, 16.86%, 25.86%, 26.86%, higher recall 27.86%, 32.54%, 27.86%, 23.95%, 19.97%, and the efficiency of the proposed method FE‐ADCNN‐GBO‐SVM is likened with the existing processes. Classification of mammograms depends features removal processes with support vector machine (FE‐LBP‐GLCM‐SVM), breast cancer classification with global discriminate features on mammographic images (FE‐GLCM‐ANN), enhancing breast cancer classification with (SMOTE) technique and pectoral muscle removal on mammographic images (FE‐SMOTE‐RF), application of artificial intelligence depends deep learning on breast cancer screening and imaging diagnosis (FE‐CNN‐CDCNN) respectively.

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Section: Introductionmentioning

confidence: 99%

“…7,8 A biopsy is standard clinic methodology utilized to diagnose early-stage cancer on microscope. 9,10 Based on subjective nature of human clarification, radiologists can have dissimilar opinions about related mammograms. 7 A false negative diagnosis phase can have serious penalties of patient.…”

mentioning

confidence: 99%