Predicting Invasive Ductal Carcinoma in breast histology images using Convolutional Neural Network

Hesham, A; Abdulmajeed, Alghodhaifi; Mohammed, Alghodhaifi

doi:10.1109/naecon46414.2019.9057822

Cited by 37 publications

(13 citation statements)

References 26 publications

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“…Segmentation is an essential step during the diagnostic, and treatment stages. Having an accurate segmentation algorithm can make a big difference in patients' life [58, 59]. Lung CT image segmentation is a necessary initial step for lung image analysis, it is a prerequisite step to provide an accurate lung CT image analysis such as lung cancer detection.…”

Section: Resultsmentioning

confidence: 99%

Object scale selection of hierarchical image segmentation with deep seeds

Al‐Huda

Peng

Yang

et al. 2020

IET Image Processing

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Hierarchical image segmentation is a prevalent technique in the literature for improving segmentation quality, where the segmentation result needs to be searched at different scales of the hierarchy to identify objects represented from various scales. In this paper, a novel framework for improving the quality of object segmentation is presented. To this end, the authors first select the optimal segments among several hierarchical scales of the input image using simple mid‐level features and dynamic programming. Simultaneously, deep seeds are localised on the input image for the foreground and background classes using a deep classification network and a saliency network, respectively. Then, a graphical model is constructed as a set of nodes that jointly propagate information from deep seeds to unmarked regions to obtain the final object segmentation. Comprehensive experiments are performed on different datasets for popular hierarchical image segmentation algorithms. The experimental results show that the proposed framework can significantly improve the quality of object segmentation at low computational costs and without training any segmentation network.

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Section: Resultsmentioning

confidence: 99%

Object scale selection of hierarchical image segmentation with deep seeds

Al‐Huda

Peng

Yang

et al. 2020

IET Image Processing

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“…Exploring the depth-wise separable convolution methodology in CNNs, Alghodhaifi et al (2019) [61] compared the performance of a standard CNN against a depthwise separable CNN for the diagnosis of IDC through 50×50 patches extracted from a total of 162 WSIs. Depth-wise separable CNNs work by applying convolution to each separate channel (in this case, there are only three channels: red, green and blue) and then combine the resulting output channels through pointwise convolution.…”

Section: A Wsi-based Segmentation Approachesmentioning

confidence: 99%

On the Dynamics and Feasibility of Transferred Inference for Diagnosis of Invasive Ductal Carcinoma: A Perspective

et al. 2022

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It is generally noticed that increasing the number of convolutional layers in generic image classification procedures proves to be detrimental to model performance in terms of validation accuracy and loss. Apart from vanilla CNNs, we have state-of-the-art (SOTA) architectures such as ResNet50 (and its variants) which show that through the use of skip-connections, higher performance metrics are attainable through deeper architectures. However, most evaluative metrics converge on a log scale as we go deeper with diminishing gradient of the metrics' curves. Given these two contrasting speculations, in this paper, we implement various vanilla and SOTA CNNs for the diagnosis of one of the most common forms of breast cancer -invasive ductal carcinoma (IDC) -to examine and understand the feasibility of implementation of SOTA CNNs through transferred weights when juxtaposed with vanilla CNNs (and LeNet-5) of varying configurations in terms of their performance metrics and other parameters. In this paper, we solve the dualobjective of studying behavioural aspects of avant-garde CNN models (more specifically, VGG16, VGG19, ResNet50, ResNet50V2, MobileNetV2, and DenseNet121) and proper diagnosis of IDC through intermediate neural activations to critically evaluate and theorize the performance of different models. We notice that among all the models, only VGG16, VGG19, LeNet-5 and a selected vanilla CNN through an optimization procedure were the ones to attain the best metrics, shared amongst them.

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“…Alghodhaifi et al [ 34 ] proposed IDCNet and IDCDNet. They implemented various activation functions, such as Sigmoid, ReLU and Tanh, to test the robustness of the models.…”

Section: Experimentation and Analysismentioning

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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Predicting Invasive Ductal Carcinoma in breast histology images using Convolutional Neural Network

Cited by 37 publications

References 26 publications

Object scale selection of hierarchical image segmentation with deep seeds

Object scale selection of hierarchical image segmentation with deep seeds

On the Dynamics and Feasibility of Transferred Inference for Diagnosis of Invasive Ductal Carcinoma: A Perspective

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

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