Classification of Mammograms Using Texture and CNN Based Extracted Features

Debelee, Taye Girma; Gebreselasie, Abrham; Schwenker, Friedhelm; Amirian, Mohammadreza; Yohannes, Dereje

doi:10.4028/www.scientific.net/jbbbe.42.79

Cited by 35 publications

(17 citation statements)

References 24 publications

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“…The summary of this and other literature is presented on Table 7. 5 Visual Geometry Group, 6 Deep Dense Inception Residual Network, 7 High Grade Glioma, 8 Low Grade Glioma.…”

Section: Deep Learning Approachmentioning

confidence: 99%

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A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

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108

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A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

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“…The summary of this and other literature is presented on Table 7. 5 Visual Geometry Group, 6 Deep Dense Inception Residual Network, 7 High Grade Glioma, 8 Low Grade Glioma.…”

Section: Deep Learning Approachmentioning

confidence: 99%

“…1 Heterogeneous CNN + Conditional Random Fields-Recurrent Regression based Neural Network,2 Deep Residual Dilate Network with Middle Supervision,3 Fully Convolutional Neural Network,4 OCcipito Module,5 Atrous-Convolution Feature Pyramid.…”

mentioning

confidence: 99%

A Survey of Brain Tumor Segmentation and Classification Algorithms

Biratu

Schwenker

Ayano³

et al. 2021

J. Imaging

Self Cite

108

View full text Add to dashboard Cite

show abstract

“…Deep learning has been applied for the classification and segmentation of medical images previously [ 28 , 29 , 30 , 31 , 32 ]. Different versions of CNNs were used for the segmentation of brain tumors from MRI scans.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Region Growing for Brain Tumor MR Image Segmentation

et al. 2021

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A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and grows and multiplies abnormally. The unusual growth of cells within the brain or inside the skull, which can be cancerous or non-cancerous has been the reason for the death of adults in developed countries and children in under developing countries like Ethiopia. The studies have shown that the region growing algorithm initializes the seed point either manually or semi-manually which as a result affects the segmentation result. However, in this paper, we proposed an enhanced region-growing algorithm for the automatic seed point initialization. The proposed approach’s performance was compared with the state-of-the-art deep learning algorithms using the common dataset, BRATS2015. In the proposed approach, we applied a thresholding technique tostrip the skull from each input brain image. After the skull is stripped the brain image is divided into 8 blocks. Then, for each block, we computed the mean intensities and from which the five blocks with maximum mean intensities were selected out of the eight blocks. Next, the five maximum mean intensities were used as a seed point for the region growing algorithm separately and obtained five different regions of interest (ROIs) for each skull stripped input brain image. The five ROIs generated using the proposed approach were evaluated using dice similarity score (DSS), intersection over union (IoU), and accuracy (Acc) against the ground truth (GT), and the best region of interestis selected as a final ROI. Finally, the final ROI was compared with different state-of-the-art deep learning algorithms and region-based segmentation algorithms in terms of DSS. Our proposed approach was validated in three different experimental setups. In the first experimental setup where15 randomly selected brain images were used for testing and achieved a DSS value of 0.89. In the second and third experimental setups, the proposed approach scored a DSS value of 0.90 and 0.80 for 12 randomly selected and 800 brain images respectively. The average DSS value for the threeexperimental setups was 0.86.

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“…k-Nearest Neighbors (k-NN) was utilized to classify mammograms as normal or abnormal. Debelee et al [ 17 ] extracted features from images using pre-trained Inception-V3 and their proposed modified adaptive K-means (MAKM) method. They collated images from the local and public datasets.…”

Section: Literature Surveymentioning

confidence: 99%

Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features

Roy

Das

Kar

et al. 2021

Sensors

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Breast cancer, like most forms of cancer, is a fatal disease that claims more than half a million lives every year. In 2020, breast cancer overtook lung cancer as the most commonly diagnosed form of cancer. Though extremely deadly, the survival rate and longevity increase substantially with early detection and diagnosis. The treatment protocol also varies with the stage of breast cancer. Diagnosis is typically done using histopathological slides from which it is possible to determine whether the tissue is in the Ductal Carcinoma In Situ (DCIS) stage, in which the cancerous cells have not spread into the encompassing breast tissue, or in the Invasive Ductal Carcinoma (IDC) stage, wherein the cells have penetrated into the neighboring tissues. IDC detection is extremely time-consuming and challenging for physicians. Hence, this can be modeled as an image classification task where pattern recognition and machine learning can be used to aid doctors and medical practitioners in making such crucial decisions. In the present paper, we use an IDC Breast Cancer dataset that contains 277,524 images (with 78,786 IDC positive images and 198,738 IDC negative images) to classify the images into IDC(+) and IDC(-). To that end, we use feature extractors, including textural features, such as SIFT, SURF and ORB, and statistical features, such as Haralick texture features. These features are then combined to yield a dataset of 782 features. These features are ensembled by stacking using various Machine Learning classifiers, such as Random Forest, Extra Trees, XGBoost, AdaBoost, CatBoost and Multi Layer Perceptron followed by feature selection using Pearson Correlation Coefficient to yield a dataset with four features that are then used for classification. From our experimental results, we found that CatBoost yielded the highest accuracy (92.55%), which is at par with other state-of-the-art results—most of which employ Deep Learning architectures. The source code is available in the GitHub repository.

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Classification of Mammograms Using Texture and CNN Based Extracted Features

Cited by 35 publications

References 24 publications

A Survey of Brain Tumor Segmentation and Classification Algorithms

A Survey of Brain Tumor Segmentation and Classification Algorithms

Enhanced Region Growing for Brain Tumor MR Image Segmentation

Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features

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