Combined Datasets For Breast Cancer Grading Based On Multi-CNN Architectures

Abdelli, Adel; Saouli, Rachida; Djemal, Khalifa; Youkana, Imane

doi:10.1109/ipta50016.2020.9286653

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…This work shows a high accuracy of 94.57% for all three classes. It concluded that the proposed methodology is robust and has high accuracy for detecting breast cancer in women as compared to [20,41]. It means that it can be implemented for real-time applications to reduce the workload and help the doctor diagnose breast cancer.…”

Section: Discussionmentioning

confidence: 94%

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A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

Alduraibi¹

2022

Intelligent Automation &Amp; Soft Computing

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This paper proposes a fused methodology based upon convolutional neural networks and a shallow classifier to diagnose and differentiate breast cancer between malignant lesions and benign lesions. First, various pre-trained convolutional neural networks are used to calculate the features of breast ultrasonography (BU) images. Then, the computed features are used to train the different shallow classifiers like the tree, naïve Bayes, support vector machine (SVM), k-nearest neighbors, ensemble, and neural network. After extensive training and testing, the DenseNet-201, MobileNet-v2, and ResNet-101 trained SVM show high accuracy. Furthermore, the best BU features are merged to increase the classification accuracy at the cost of high computational time. Finally, the feature dimension reduction ReliefF algorithm is applied to address the computational complexity issue. An online publicly available dataset of 780 BU images is utilized to validate the proposed approach. The dataset was further divided into 80 and 20 percent ratios for training and testing the models. After extensive testing and comprehensive analysis, it is found that the DenseNet-201 and Mobile-Net-v2 trained SVM has an accuracy of 90.39% and 94.57% for the original and augmented BU images online dataset, respectively. This study concluded that the proposed framework is efficient and can easily be implemented to help and reduce the workload of radiologists/doctors to diagnose breast cancer in female patients.

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Section: Discussionmentioning

confidence: 94%

“…In a study [41], the concept of transfer learning is utilized for training the pre-trained model for the classification of BU images. The ResNet-50 and MobileNet pre-trained models showed 97.03% and 94.42% accuracy and took almost 114.57 and 192.4 min for training, respectively.…”

Section: Discussionmentioning

confidence: 99%

A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

Alduraibi¹

2022

Intelligent Automation &Amp; Soft Computing

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“…These techniques, on the other hand, are robust and necessitate a large amount of computer power. Transfer learning, on the other hand, is becoming increasingly common; for example, many studies 26,27 used transfer learning to grade breast cancer. There is a knowledge gap among these research, to our knowledge: there have been no performance comparisons of recent pre-trained state-of-the-art CNN architectures ((EfficientNetB0 28 , EfficientNetV2B0 29 , EfficientNetV2B0-21k 29 , ResNetV1-50 30 , ResNetV2-50 31 , MobileNetV1 32 , and MobileNetV2 33 ).…”

Section: Performance Analysis Of Seven Convolutional Neural Network (...mentioning

confidence: 99%

“…2. To conduct a comparative investigation of the performance of seven selected cutting edge CNN architectures on the Four Breast Cancer Grades (FBCG) Dataset 26 .…”

Section: Performance Analysis Of Seven Convolutional Neural Network (...mentioning

confidence: 99%

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images

Voon

Hum

Tee

et al. 2022

Sci Rep

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Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.

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“…In recent years, deep learning showed good results in image classification, especially in object detection by the use of the convolutional neural networks (CNNs) architectures which are the leading algorithms in BC classification, [7][8][9] brain tumor segmentation, 10,11 and in many other computer vision tasks. [12][13][14][15][16] Although CNN succeeded in classifying images, they still need more improvement to avoid the overfitting, under-fitting, and the use of the different preprocessing techniques that take the time and the resources in classifying histopathological and microscopic images.…”

Section: Introductionmentioning

confidence: 99%

Multiple instance learning for classifying histopathological images of the breast cancer using residual neural network

Abdelli

Saouli

Djemal

et al. 2022

Int J Imaging Syst Tech

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Digital histopathological images have complex textures and high variability. Thus, classifying histopathological images requires an accurate classification and recognition of the tissue components in these images. In this article, we propose a novel classification layer based on multiple instances learning (MIL). In regular convolutional neural network (CNN) a flatten or a global pooling layer is used before the fully connected layers. However, in our proposed layer, we consider each last feature map in the network as an instance that will be classified by the output layer. Then, an aggregation function will be applied to get the class of the image (bag). This mapping helps the model to classify each feature independently to catch the micro-objects of the complex tissue images. Also, our method succeeded in achieving high accuracy without the preprocessing of the images with color normalization, stain normalization, or any other techniques. Additionally, we trained our models in two different strategies. The first one is by combining the images from all the magnification factors, and the second is by training a model for each magnification factor. We show in this work that our model outperforms several previous works on breast cancer classification.

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Combined Datasets For Breast Cancer Grading Based On Multi-CNN Architectures

Cited by 8 publications

References 18 publications

A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images

Multiple instance learning for classifying histopathological images of the breast cancer using residual neural network

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