An Intelligent Classification of Breast Cancer Images

Durgadevi, G.; Shekhar, Himanshu

doi:10.17485/ijst/2016/v9i28/86071

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…During most pathological analyses, the pathologist is interested in identifying specific parts of the anatomical area. Using the dictionary learning framework, Durgadevi and Shekhar designed a dual C GAN network to identify tumor regions in the pathological images of cancer patients [11]. Sinha based on convolution neural network to colonic gland pathological image segmentation, by studying the characteristics of the colonic gland pathological image, so as to realize the automatic segmentation of the gland area, proposed method that can detect the target area, but the split between different gland adhesion situation and design of network structure remains to be optimized [12].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Segmentation and Recognition Method of Breast Cancer Based on Digital Image Processing Technology

Tang

2021

Wireless Communications and Mobile Computing

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With the development of digital image technology, judging diseases by medical image plays an important role in medical diagnosis. Mammography is the most effective imaging examination method for breast cancer at present. Intelligent segmentation and identification of breast cancer images and judging their size and classification by digital image processing technology can promote the development of clinical medicine. This paper introduces the preprocessing technology of breast cancer pathological image and medical image recognition technology of breast cancer. In order to improve the segmentation accuracy of image processing and optimize, the segmentation recognition ability in digital mammography was improved. Based on the technical basis of pathological image analysis of breast cancer, the architecture of intelligent segmentation and recognition system for breast cancer was constructed, and each functional module of intelligent system was introduced in detail. Based on digital image processing technology, filtering technology is used to reduce dryness and improve the clarity of the image. Public datasets INBreast and DDSM-BCRP were used to verify system’s performance, and it was tested on the breast cancer image test set. The experiment shows that the comprehensive performance of the intelligent segmentation and recognition system can realize the segmentation and recognition of breast cancer and has higher accuracy and interpretability, which is helpful to improve the diagnosis of doctors.

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Section: Related Workmentioning

confidence: 99%

Intelligent Segmentation and Recognition Method of Breast Cancer Based on Digital Image Processing Technology

Tang

2021

Wireless Communications and Mobile Computing

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“…A system [12] was proposed to classify malignant and benign images of mammograms. The extracted statistical features were classified by using ANN (Artificial Neural Networks) and reported classification accuracy of the system is 94%.…”

Section: Related Workmentioning

confidence: 99%

Intelligent System for Detection of Micro-Calcification in Breast Cancer

Rehman¹,

Ahmed²,

Ahmed³

2017

ijacsa

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Abstract-Recently; medical image mining has become one of the well-recognized research area(s) of machine learning and artificial intelligence techniques have been vastly used in various computer added diagnostic systems. Specifically; breast cancer classification problem is considered as one of the most significant problems. For instance, complex, diverse and heterogamous malignant features of micro-calcification in DICOM (Digital Communication in Medicine) images of mammography are very difficult to classify because the persistence of noise in mammogram images creates lots of confusions for doctors. In order to reduce the chances of misdiagnosis and to discernment the difference between malignant and benign lesions of microcalcification this paper proposes a system so called "Intelligent System For Detection of Micro-Calcification in Breast Cancer" by considering all above stated problems. Overall our system comprises over three main stages. In first stage, adaptive threshold algorithm is used to reduce the noise, and canny edge detection algorithm is used to detect the edges of every macro or micro classification. In second stage, deginated as feature selection is done by using auto-crop algorithm, which crops all types of calcifications and lesions by proposed algorithm so called CFEDNN (Calcification Feature Extraction Deep Neural Networks) which is designed to avoid the manual ROIs (Region of Interest). Decision model is constructed by using DNN (Deep Neural Networks) and the best classification accuracy is measured as 95.6%.

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An Investigation on Different Approaches for Medical Imaging

Haldorai,

Murugan

et al. 2024

EAI/Springer Innovations in Communication and Computing

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An Intelligent Classification of Breast Cancer Images

Cited by 5 publications

References 18 publications

Intelligent Segmentation and Recognition Method of Breast Cancer Based on Digital Image Processing Technology

Intelligent Segmentation and Recognition Method of Breast Cancer Based on Digital Image Processing Technology

Intelligent System for Detection of Micro-Calcification in Breast Cancer

An Investigation on Different Approaches for Medical Imaging

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