Breast cancer detection: A review on mammograms analysis techniques

Boulehmi, Hela; Mahersia, Hela; Hamrouni, Kamel; Boussetta, Samia; Mnif, N.

doi:10.1109/ssd.2013.6563999

Cited by 34 publications

(13 citation statements)

References 62 publications

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“…Studies have presumed that early discovery and proper treatment of infection will expand the survival rate [2]. Breast growth is less demanding to treat, with less dangers and diminishes mortality by 25% when breast abnormality detected at early stage.…”

Section: Associated Effortmentioning

confidence: 99%

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Sensor Enabled Wearable Technological Tool to Detect Breast Abnormality

Kavitha¹,

Raja²

2018

IJET

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Our Historical records say that, Women are back bone to every family. Generally we observe that health risk rate in women increases depends on age. Statistical medical reports proven that, early identification of health risks will increase the survival rate of patient. From last sixty years, we heard that breast cancer is one of the big health dare for women after forty years of age. Early phase detection of breast tumor is a big research issue in both developed and developing countries. Due to the limits in existing breast abnormality detecting tools like mammography, thermography, ultrasound, MRI there is a need to introduce smart, cost effective, patient comfortable, nonionic radiated compact testing tool to detect breast abnormality at early stage. In this work I am going to propose an advanced sensor enabled wearable technological tool design to detect breast abnormality. This tool is portable, patient comfortable, light weight, cost effective and suitable for dense breast, pregnant women and surgically altered breasts, younger women also.

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Section: Associated Effortmentioning

confidence: 99%

“…Early identification and suitable treatment of breast cancer altogether increment the odds of existence [2]. The available breast tumor analytical methods are mammography, clinical inspection, thermography, ultrasound, MRI and biopsy etc.…”

Section: Introductionmentioning

confidence: 99%

Sensor Enabled Wearable Technological Tool to Detect Breast Abnormality

Kavitha¹,

Raja²

2018

IJET

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“…It scans the breast from two primary view i.e. Medio lateral oblique view and craniocaudally view [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Breast Segmentation and Probable Region Identification for Breast Cancer using DL-CNN

Kumar M*,

Jatti,

Narayanappa

2019

IJITEE

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Mammography is one of the key method used for detecting the breast cancer, several researcher has proposed the detection and segmentation method, however absolute solution has not developed till now and they have certain limitation and still it is one of the major challenge for finding the region in masses. Hence in this research work we have developed and design a novel method named as DL-CNN (Dual Layered) architecture CNN. The main intention of the model is segmentation and probable region identification. DL-CNN is based on the Convolution Neural Network. It has two layer first layer is applied for identifying the probable region whereas the second layer is used for segmentation and minimizing the false positive Reduction. In order to evaluate the DL-CNN algorithm by using the In Breast Dataset. Moreover the proposed model is compared against the various model in terms of ROI(Region of Interest), Dice_Index and False positive per Image. Result analysis shows that our model outperforms the existing

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“…This is a very simple current statistics to show that breast cancer is one alarming disease condition where still medical science is lagged behind for performing an efficient detection of it in early stage [3]. Although, there are some good number of work carried out towards cancer detection [4] [5], but still there is no reported cases to claim 100% successful identification owing to various causes associated with capturing of radiological images. Therefore, this problem offers a significant impediment towards exploring better possibility of performing classification of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Novel Framework for Breast Cancer Classification for Retaining Computational Efficiency and Precise Diagnosis

Vidya¹,

Kurian²

2018

CAE

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Classification of breast cancer is still an open-end challenge in medical image processing. The existing literatures were reviewed to found that existing solution are more pivotal towards accuracy in classification and less towards achieving computational effectiveness in classification process. Therefore, this paper presents a novel classification approach that bridges the trade-off between computational performances of classifier with its final response towards disease criticality. An analytical framework is built that takes the input of Magnetic Resonance Imaging (MRI) of breast cancer which is subjected to non-linear map-based filter for enhancing pre-processing operation. The algorithm also offers a novel integral transformation scheme that lets the filtered image to get itself transformed followed by precise extraction of foreground and background for assisting in reliable classification. A statistical-based approach is used for extracting feature followed by classifying using unsupervised learning algorithm. The study outcome shows superior performance compared to existing schemes of classification.

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Breast cancer detection: A review on mammograms analysis techniques

Cited by 34 publications

References 62 publications

Sensor Enabled Wearable Technological Tool to Detect Breast Abnormality

Sensor Enabled Wearable Technological Tool to Detect Breast Abnormality

Breast Segmentation and Probable Region Identification for Breast Cancer using DL-CNN

Novel Framework for Breast Cancer Classification for Retaining Computational Efficiency and Precise Diagnosis

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