Histopathological breast-image classification with image enhancement by convolutional neural network

Nahid, Abdullah-Al; Ali, Ferdous Bin; Kong, Yinan

doi:10.1109/iccitechn.2017.8281815

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Edge enhancement can highlight object structures, boundaries and textures, facilitating the identification of different types of objects and the delineation of their distribution ranges. Nahid et al [18] proposed histopathological breast-image classification with multiscale retinex enhancement by CNN. Instead of using the raw images directly, they normalised the image by applying the multiscale retinex algorithm to improve the local contrast and illumination variation.…”

Section: Related Workmentioning

confidence: 99%

“…Coincidentally, image contents can be roughly divided into lowfrequency and high-frequency contents. Image enhancement in the traditional sense mainly refers to high-frequency enhancement [14][15][16][17][18]32]. However, in image classification, low-frequency characteristics are also beneficial for image classification.…”

Section: Low-frequency and High-frequency Enhancementsmentioning

confidence: 99%

“…There are few works on feature enhancement filters in CNNs. The research achievements mainly involve feature enhancement in specific fields, such as the recognition of character [14], fingerprint [15] and medical images [16–18], thus lacking wide adaptability. The feature enhancement in the works actually belongs to the category of image enhancement, which focuses on image enhancement based on the images themselves, without taking CNN into account.…”

Section: Introductionmentioning

confidence: 99%

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Research on adaptive local feature enhancement in convolutional neural networks

et al. 2020

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Local feature extraction is one of the key characteristics of convolutional neural networks (CNNs). This study proposes an adaptive local feature enhancement (ALFE) model with a low-frequency general appearance-enhancement operator and a high-frequency local detail enhancement operator to improve local features of CNNs. Through supervised training, the model could adaptively adjust enhancement parameters and achieve a global-local enhancement of training images and CNNs. The performance of ALFE was first preliminarily evaluated with a self-built CNN on CIFAR-10 data set in different conditions of image augmentation and feature pooling. CNNs with ALFE could increase the top-1 accuracy compared against CNNs in the same conditions, and nearly reach the same level of performance for different pooling approaches. With only two extra adjustable parameters, this model could effectively avoid overfitting, without affecting the convergence speed of CNN. In addition, the extra burden of network complexity could be neglected. Further, experiments of three existing popular CNNs (AlexNet, VGGNet and ResNet) with ALFE were carried out on dogs versus cats, Tiny ImageNet, and SVHN data sets, respectively. The results show that ALFE is feasible for the existing popular CNN models, improving their top-1 accuracies without changing their convergence speeds.

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Section: Related Workmentioning

confidence: 99%

Section: Low-frequency and High-frequency Enhancementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on adaptive local feature enhancement in convolutional neural networks

et al. 2020

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“…P. Casti et al [32] used QDA-LDA model for auto-localization and classification of asymmetry ROI because it directly related to the accuracy of doctor's predicting and treatment A.-A. Nahid et al [33] proposed an approach that extracts ROI patches from HP images for the classification of invasive and noninvasive breast cancer by CNN. B. E. Bejnordi et al [34] and Y. Feng et al [35] performed a biopsy to classify the breast WSIs into different categories through the deepconvolution neutral network and achieves the highest accuracy in binary-classification of cancerous slides.…”

Section: Introductionmentioning

confidence: 99%

A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities

Mahmood

Pei

et al. 2020

IEEE Access

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Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.

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