Computer Aided System for Detection and Classification of Breast Cancer

Shanthi, S. Anita; Bhaskaran, V. Murali

doi:10.5121/ijitca.2012.2408

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…Statistical parameters are a technique of taking image contents from image. The major intention of feature extraction method is to symbolize original image in its condensed form for the purpose of facilitating choice making manner including pattern class [7]. Texture parameter is obtained from the mammograms which are employed to trim the classification result.…”

Section: Statistical Parameter Analysismentioning

confidence: 99%

Premature interlude detection and classification of breast cancer using ANN classifier

Sruthi¹,

Devi²,

Balamurugan³

2017

IJET

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Breast cancer has emerged as the main reason behind most cancers deaths amoungwomen. To decrease the emerging issue, cancer should be handled at the early stage, however it's extremely complicated to discover associated diagnose tumors at a premature stage. Manual analysis of cancer is found to be extremely time consumingprocess andincompetent in several scenarios. As a result, there exists a choice for sensibleschemes that identifies the cancerous cell,simultaneouslydeprived of any participation of people and with excessive accuracy. Here, formulated automatic method victimization Artificial Neural Network (ANN)as better intellectual system for breast cancer classification. Image Processingtakes part avitalplace in cancer recognition once input document is inside the style of pixels. Feature extraction of image could be very vital in Mammogram classification. Alternatives feature extraction methods have been developed recently. An absolutely distinctive function extraction method isused for classification of conventional and Normal cancer image classification. This methodology can offer maximum accuracy at a high speed. The applied math parameter encompass entropy, mean, power, correlation, texture, variance .This constraints can act as a inputs to ANN which is adequate enough to identify and provides the outcome whether or not patient is suffering from cancerous or not.

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Section: Statistical Parameter Analysismentioning

confidence: 99%

Premature interlude detection and classification of breast cancer using ANN classifier

Sruthi¹,

Devi²,

Balamurugan³

2017

IJET

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“…Proposed system's precision, recall and F-means compared with four classifiers (RBFN, MLP, Navie bayes and C4.5) and at the result the proposed system had the highest precision, recall and F means for classification of image as normal, benign or malignant. With this system early detection of breast cancers improved (Shanthi and Bhaskaran, 2012). In other research, applied image processing threshold, edge-based and watershed segmentation for mammogram breast cancer images; and test three algorithms with MatLab software.…”

Section: Experiences In Breast Cancer Diagnosis Through Image Processingmentioning

confidence: 99%

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

Mohammadzadeh

Safdari²,

Ghazisaeidi³

et al. 2015

Asian Pacific Journal of Cancer Prevention

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Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

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“…Shanthi [9] presents a complete method for automatic detection and classification of suspicions regions in mammogram images, using Fuzzy C-Means clustering for identify suspicious regions automatically. Co-occurance matrix and wavelet energy features of segmented regions was used for trainning a self adaptative resource allocation network.…”

Section: Related Workmentioning

confidence: 99%

A telediagnostic system for automatic detection of lesions in digital mammograms

Silva

Barros

Santana

2014

5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC)

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Breast cancer is the second type of cancer that affects women in the world, losing only for non melanoma skin cancer. The mammography is the most accurate exam for the diagnostic of this disease, allowing to discovery in its initial stage and increasing the chance of prompt treatment. This paper presents the modeling and implementation of Web based telediagnostic system for automated detection and register of lesions in mammographic images, based on Independent Component Analysis (ICA) and Support Vector Machine (SVM). The system analyses digital mammography images submited for Internet providing an image diagnosis and indicating the presence of suspicious regions, which can be evaluated and treated by an specialist. Also presents the methodology for development of proposed system. A system's prototype was developed to run tests that could measure its efficiency. Mini-MIAS was the training database used to test the algorithms. We also used SMV to classify image interest regions as normal or suspicious. Test results showed that 89.13% of normal samples were correctly classified and 92.17% of suspicious samples were also classified apropiately. We note that the proposed method provides support to detect suspicious regions in mammography images, incorporated in a automated CAD system.

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Computer Aided System for Detection and Classification of Breast Cancer

Cited by 10 publications

References 20 publications

Premature interlude detection and classification of breast cancer using ANN classifier

Premature interlude detection and classification of breast cancer using ANN classifier

Advances in Optimal Detection of Cancer by Image Processing; Experience with Lung and Breast Cancers

A telediagnostic system for automatic detection of lesions in digital mammograms

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