Classification of Breast Lesions from Mammograms

Jiang, Yulei

doi:10.1016/b978-012077790-7/50027-8

Cited by 7 publications

(1 citation statement)

References 70 publications

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“…One of the reasonable approaches to describe the abnormalities is by human perception. While the radiologists have special training to identify the state of the tissue in the breast from its appearance in a mammogram, many CAD systems used the features described by radiologists to build and train the proposed systems, although they used different terminologies for the same features [ 46 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

AlShammari

Almuhanna

Alhiyafi

2021

Sensors

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A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes.

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Section: Proposed Methodsmentioning

confidence: 99%

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

AlShammari

Almuhanna

Alhiyafi

2021

Sensors

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Design of a High-Accuracy Classifier Based on Fisher Discriminant Analysis: Application to Computer-Aided Diagnosis of Microcalcifications

Hamdi

Auhmani

Hassani

2008

2008 International Conference on Computational Sciences and Its Applications

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In this paper we present a high accuracy computer-aided diagnosis scheme. The goal of the developed system is to classify benign and malignant microcalcifications on mammograms. It is mainly based on a combination of wavelet decomposition, feature extraction and classification methodology using Fisher's linear discriminant. The contribution of wavelet decomposition is to denoise and to enhance regions of interests (ROI) containing abnormalities. Feature extraction is performed using spatial grey level dependence (SGLD) matrices. The purpose of classification is to assign an object to a certain class. Many classification methods have been described. Here we use Fisher's linear discriminant. Fisher's linear discriminant is particularly useful for discriminating between two classes in a multidimensional space. Since it is based only on the first and second moments of each distribution, it is not a computationally intensive method. Our results show that the developed method is effective for quantifying the classification of benign and malignant microcalcifications abnormalities with an accuracy of 95.5%.

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A survey on detection of micro calcification in mammogram images

Manikandan¹,

Surendar²

2017

2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE)

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Classification of Breast Lesions from Mammograms

Cited by 7 publications

References 70 publications

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

Design of a High-Accuracy Classifier Based on Fisher Discriminant Analysis: Application to Computer-Aided Diagnosis of Microcalcifications

A survey on detection of micro calcification in mammogram images

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