Histopathological Breast-Image Classification Using Concatenated R–G–B Histogram Information

Nahid, Abdullah-Al; Kong, Yinan

doi:10.1007/s40745-018-0162-3

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…It is worth mentioning that works ( 35 – 43 ) did not split training and test set according to the protocol of ( 9 ), works ( 44 , 45 ) adopted the existed protocol, and works ( 46 , 47 ) randomly divided training set (70%) and test set (30%), but they did not mention whether it was the same as the protocol. Although the recognition accuracy of the works ( 37 , 39 , 41 – 43 , 46 , 47 ) is significantly higher than that of our method, they all use deep learning model, which requires a large number of labeled training samples and consumes longer training time. In addition, in these works, except ( 42 ), they only calculated the image level recognition accuracy.…”

Section: Experiments and Resultsmentioning

confidence: 84%

“…Table 8 shows that the method proposed in this paper is superior to many state-of-the-art methods in benign and malignant tumor recognition, both for the image level and the patient level. It is worth mentioning that works (35)(36)(37)(38)(39)(40)(41)(42)(43) did not split training and test set according to the protocol of (9), works (44,45) adopted the existed protocol, and works (46,47) randomly divided training set (70%) and test set (30%), but they did not mention whether it was the same as the protocol. Although the recognition accuracy of the works (37, 39, 41-43, 46, 47) is significantly higher than that of our method, they all use deep learning model, which requires a large number of labeled training samples and consumes longer training time.…”

Section: Experiments Resultsmentioning

confidence: 99%

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Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

Qiao

Zhang

et al. 2021

Front. Oncol.

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Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.

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Section: Experiments and Resultsmentioning

confidence: 84%

Section: Experiments Resultsmentioning

confidence: 99%

Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

Qiao

Zhang

et al. 2021

Front. Oncol.

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show abstract

“…Regardless of the used model, the distribution of intensities in each color channel is represented by a histogram of 256 bins (for images coded on 8 bits). These histograms can be concatenated and used as color descriptor of (3 × 256) values [17], [22]. Instead of concatenating the three histograms independently, it is also possible to generate a 3D histogram representing the joint distribution of the three channels.…”

Section: A Color Histogram Featuresmentioning

confidence: 99%

Significant Smartphone Images Features For Liver Steatosis Assesment

Amer¹,

Magnier²,

Janaqi³

et al. 2021

2021 IEEE International Conference on Imaging Systems and Techniques (IST)

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The growing demand for liver transplantation is leading medicine to explore new technologies in order to extend the field of viable transplants. Automatic hepatic steatosis assessment is the first step towards the development of a computeraided liver diagnosis due to its importance as a risk factor for primary dysfunction. Color and texture are considered as two fundamental visual characteristics to assess the degree of hepatic steatosis. The aim of this study is to determine the discriminating features for liver image classification according to three steatosis classes: mild, moderate or severe. First, color features are extracted in three color spaces namely: RGB, HSV and YCbCr. Texture features are then extracted from RGB components using co-occurrence matrices, Local Binary Pattern (LBP) and Local Phase Quantization (LPQ). Additionally, feature-extraction was followed by generating a linear model regression using Least Absolute Shrinkage and Selection Operator (LASSO) allowing the classification task. The experimental results show that RGB histograms provide better classification accuracy. However, the relevance of these preliminary results is not sufficient to draw a decisive conclusion due to the experimental database, which is really small, very unbalanced and the images were captured under unknown and widely varying conditions.

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“…This study found that performing the grayscale transformation, as a stain normalization method, decreases the accuracy of the results. Study [52] claims that conventional normalization techniques increase the noise in the image and introduce a new normalization technique that controls the noise.…”

Section: Binary Classificationmentioning

confidence: 99%

Deep Learning Applied for Histological Diagnosis of Breast Cancer

Yari

Nguyen

2020

IEEE Access

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Deep learning, as one of the currently most popular computer science research trends, improves neural networks, which has more and deeper layers allowing higher abstraction levels and more accurate data-based. Although deep convolutional neural networks, as a deep learning algorithm, has recently achieved promising results in data analysis, the requirement for a large amount of data prevents its use in medical data analysis since it is challenging to obtain data from the medical field. Breast cancer is a common cancer in women. To diagnose this kind of cancer, breast cell shapes in histopathology images should be examined by senior pathologists. The number of pathologists per population in the world is not enough, especially in Africa, and human mistake may occur in diagnosis procedure. After the evaluation of deep learning methods and algorithms in breast histological data processing, we tried to improve the current systems' accuracy. As a result, this study proposes two effective deep transfer learning-based models, which rely on pre-trained DCNN using a large collection of ImageNet dataset images that improve current state-of-the-art systems in both binary and multiclass classification. We transfer pre-trained weights of the ResNet50 and DesneNet121 on the Imagenet as initial weights and fine-tune these models with a deep classifier with data augmentation to detect various malignant and benign samples tissues in the two categories of binary classification and multiclass classification. The proposed models have been examined with optimized hyperparameters in magnification-dependent and magnification-independent classification modes. In the multiclass classification, the proposed system achieved up to 98% accuracy. As for binary classification, the proposed system provides up to 100% accuracy. The results outperform previous studies accuracies in all defined performance metrics in breast cancer CAD systems from histological images.

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Histopathological Breast-Image Classification Using Concatenated R–G–B Histogram Information

Cited by 8 publications

References 25 publications

Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

Significant Smartphone Images Features For Liver Steatosis Assesment

Deep Learning Applied for Histological Diagnosis of Breast Cancer

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