Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques

Mabrouk, Mai S.; Afify, Heba M.; Marzouk, Samir Y.

doi:10.1016/j.asej.2019.01.009

Cited by 31 publications

(17 citation statements)

References 22 publications

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“…Traditional CAD methods usually need to manually extract features from images [ 7 ]. These features include original features such as shape and texture [ 8 , 9 ], and the features extracted from the original features by machine learning algorithms, such as Histogram of Gradient [ 10 – 12 ], Local Binary Patter [ 13 , 14 ] and Gabor filter [ 11 , 12 ]. However, the selection and combination of features largely depend on the experience of designers, so the traditional methods have some limitations.…”

Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologists. Therefore, the main purpose of this study is to perform two-stage classification (Normal/Abnormal and Benign/ Malignancy) of two-view mammograms through convolutional neural network. In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature extraction. The model consists of two independent branches, which are used to extract the features of two mammograms from different views. Our work mainly focuses on the construction of multi-scale convolution module and attention module. The final experimental results show that the model has achieved good performance in both classification tasks. We used the DDSM database to evaluate the proposed method. The accuracy, sensitivity and AUC values of normal and abnormal mammograms classification were 94.92%, 96.52% and 94.72%, respectively. And the accuracy, sensitivity and AUC values of benign and malignant mammograms classification were 95.24%, 96.11% and 95.03%, respectively.

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Section: Introductionmentioning

confidence: 99%

New convolutional neural network model for screening and diagnosis of mammograms

et al. 2020

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“…Researchers obtained an accuracy of 91.37% and 93.22% in the MIAS and DDSM datasets, respectively. Many studies have exploited the integration between different features of shape and texture to improve the CAD system for the classification of breast cancer [46][47][48].…”

Section: Related Workmentioning

confidence: 99%

“…Our proposed model yields a 100% performance result in terms of all used matrices in the context of classification using 316 images out of 322 from the Mini-MIAS database. The previous study [47] obtained 100% sensitivity, whereas the work of [57] used hybrid LB-GLCM+LPQ texture features, and fewer images were used for the evaluation in both works. Moreover, we also found that the study of [57] gives 100% accuracy where this work used LWT+PCA (32 features), and 119 mammogram images from Mini-MIAS were evaluated.…”

Section: Comparision With Other Techniquesmentioning

confidence: 99%

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Zebari

Ibrahim²,

Zeebaree

et al. 2021

Applied Sciences

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Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.

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“…A computer-aided microcalcification detection model was developed using the firefly algorithm and extreme learning by S. R et al [ 12 ]. Mabroul et al [ 13 ] developed a breast cancer diagnostic system for microcalcification detection from mammograms based on shape features.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Rehman

Pei

et al. 2021

Sensors

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Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.

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Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques

Cited by 31 publications

References 22 publications

New convolutional neural network model for screening and diagnosis of mammograms

New convolutional neural network model for screening and diagnosis of mammograms

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

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