A new interval differential equation for edge detection and determining breast cancer regions in mammography images

Guo, Guangxing; Razmjooy, Navid

doi:10.1080/21642583.2019.1681033

Cited by 34 publications

(7 citation statements)

References 45 publications

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“…In this study, we utilize feature extraction to benefit from its advantages and for providing more accurate outcomes. The current research uses feature extraction for obtaining valuable data from the input images [ 30 ]. For this purpose, several features have been utilized.…”

Section: Methodsmentioning

confidence: 99%

“…The main objective is minimizing the integration function based on the multiplication, including accuracy of categorization and F-score. The cost function is given below:

where,

describes the classification accuracy rate and is achieved as equations [ 30 , 28 ]:

where,

defines the number of test sets,

shows the class of item

.…”

Section: Methodsmentioning

confidence: 99%

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An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

Liu,

Kawashima,

Rezaei sofla

2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

“…The main objective is minimizing the integration function based on the multiplication, including accuracy of categorization and F-score. The cost function is given below:

where,

describes the classification accuracy rate and is achieved as equations [ 30 , 28 ]:

where,

defines the number of test sets,

shows the class of item

.…”

Section: Methodsmentioning

confidence: 99%

An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

Liu,

Kawashima,

Rezaei sofla

2023

Heliyon

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“…To locate the edges, Laplacian approach searches for zero crossings in the 2nd derivative of the image. The most widely utilized discontinuitybased edge detection approaches, as indicated below [11][12][13].…”

Section: Traditional Edge Detection Techniquesmentioning

confidence: 99%

Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

Taher,

Ghanim,

Der

2023

Al-Mustansiriyah Journal of Science

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Medical image processing has become one of the crucial elements of the diagnostic process because of the increased usage of medical imaging recently, and clinicians' dependence on such computer-processed medical images in diagnosing patients. As the traditional Canny edge detection algorithm is sensitive to noise, it is easy to lose weak edge information when filtering out the noise, and its fixed parameters show poor adaptability. The suggested algorithm introduced the concept of image block intensity operator to replace image gradient. In addition, the computing speed of the suggested algorithm is relatively fast because it works block by block rather than pixel by pixel. Two adaptive threshold selection methods are presented, one based on the median accumulative histogram of image gradient magnitude and the other on the standard deviation for both types of image pixels (one with less edge information and the other with rich edge information). The proposed algorithm can be dividing into four stages: Input the medical digital image, convert the color medical image to gray-scale, applied improved canny edge detection, then calculate the MSE & PSNR Measures, in addition conduct a visual questionnaire by oncologists to find out which method that made the enhancement of the medical image clearer.

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“…Here, a screening process has been performed using digital mammographic data followed by three more steps, namely, extraction, selection, and classification. Currently, in machine learning algorithms, artificial intelligence is used in the classification of images [21][22][23][24][25]. Breast cancer detection is done using deep convolution neural network (CNN) with semi supervised graph algorithm.…”

Section: Related Workmentioning

confidence: 99%

Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

Prakash¹,

Kumar²,

Ram³

et al. 2022

Computer Systems Science and Engineering

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Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study, we introduce a new hybrid wavelet filter for accurate image enhancement. The main objective of enhancement is to produce quality images for detecting cancer sections in images. Image enhancement is the main step where the quality of the input image is improved to detect cancer masses. In this study, we use a combination of two filters, namely, Gabor and Legendre. The edges are detected using the Canny detector to smoothen the images. High-quality enhanced image is obtained through the Gabor-Legendre filter (GLFIL) process. Further image is used by classification algorithm. Animal migration optimization with neural network is implemented for classifying the image. The output is compared to existing filter techniques. Ultimately, the accuracy achieved by the proposed technique is 98%, which is higher than existing algorithms.

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A new interval differential equation for edge detection and determining breast cancer regions in mammography images

Cited by 34 publications

References 45 publications

An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

Applied Improved Canny Edge Detection for Diagnosis Medical Images of Human Brain Tumors

Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

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