Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Dong, Min; Wang, Zhe; Cheng-hui, Dong; Mu, Xiaomin; Ma, Yide

doi:10.1155/2017/3213680

Cited by 18 publications

(8 citation statements)

References 36 publications

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“…In addition to geometrical properties, contours obtained from a modified Single Univalue Segment Assimilating Nucleus filtered mammogram were also used in the extraction procedure. Literature is replete with different ROI extraction techniques which are not based on deep learning techniques [31]- [34]. Similarly, Xiang, et al [35] claimed that they were able to automate the extraction of ROIs even from noisy medical images using an extraction algorithm with statistical moments.…”

Section: A Image Preprocessing: Segmentation and Croppingmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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Breast cancer is a type of cancer that has risen to be the second cause of death among women. Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries. Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern. It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality. Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding. The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection. As a result, little research interest has been recorded in this area. It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease. However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease. To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade. To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images. Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD. Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated. Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-ofthe-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD. The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions. This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.

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Section: A Image Preprocessing: Segmentation and Croppingmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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“…Here, h m C is the mean of the sub-band coefficients of the layer, h C max is the maximum value of the sub-band coefficients of the layer, 1 T is the standard deviation of the coefficients, and T is the…”

Section: Coefficient Operation Processing High Frequency Coefficientmentioning

confidence: 99%

“…In recent years, the incidence and mortality of breast cancer have increased to a great extent, and it has become a major public health problem [1]. According to a report released by the International Agency for Research on Cancer Research (IARC) in 2012, more than 1.67 million women are affected by breast cancer every year in the world, and this number is growing [2].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

Dong

Sun

et al. 2019

Applied Sciences

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Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively.

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“…Mammography is the most effective, reliable, has low cost and less harmful screening for early detection of breast cancer [8]. Automatic CAD with mammography has a higher accuracy rate of detection, operation convenient speeds up the diagnostic procedure, and saves the medical resources [9]. Further, the breast mass plays a distinctive sign in breast cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram

Malebary

Hashmi

2021

IEEE Access

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In recent years, deep learning techniques are employed in the mammography processing field to reduce radiologists' costs. Existing breast mass classification systems are implemented using deep learning technologies such as a Convolutional Neural Network (CNN). CNN based systems have attained higher performance than the machine learning-based systems in the classification task of mammography images, but a few issues still exist. Some of these issues are; ignorance of semantic features, analysis limitation to the current patch of images, lost patches in less contrast mammography images, and ambiguity in segmentation. These issues lead to increased false information about patches of mammography image, computational cost, decisions based on current patches, and not recovering the variance of patches intensity. In turn, breast mass classification systems based on convolutional neural networks produced unsatisfactory classification accuracy. To resolve these issues and improve the accuracy of classification on low contrast images, we propose a novel Breast Mass Classification system named BMC. It has improved architecture based on a combination of k-mean clustering, Long Short-Term Memory network of Recurrent Neural Network (RNN), CNN, random forest, boosting techniques to classify the breast mass into benign, malignant, and normal. Further, the proposed BMC system is compared with existing classification systems using two publicly available datasets of mammographic images. Proposed BMC system achieves the sensitivity, specificity, F-measure, and accuracy for the DDSM dataset is 0.97%, 0.98%,0.97%, 0.96% and for the MIAS dataset is 0.97%, 0.97%,0.98%, and 0.95% respectively. Further Area Under Curve (AUC) rate of the proposed BMC system lies between 0.94% -0.97% for DDSM and 0.94%-0.98% for the MIAS dataset. The BMC method worked comparably better than other mammography classification schemes that have previously been invented. Moreover, the Confidence interval statistical test is also employed to determine the classification accuracy of the BMC system using different configurations and neural network parameters.INDEX TERMS Deep learning, convolutional neural network, random forest, breast mass classification, recurrent neural network, boosting, machine learning, mammograms, medical image processing.

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Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Cited by 18 publications

References 36 publications

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram

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