Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network

Al‐Haija, Qasem Abu; Adebanjo, Adeola

doi:10.1109/iemtronics51293.2020.9216455

Cited by 91 publications

(26 citation statements)

References 12 publications

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“…The ResNet-50 CNN was used to automatically extract features from each muzzle image [56]. Selection of ResNet-50 was based on the fact that (i) it is a top-performer on object classification tasks [57][58][59], (ii) the fact its depth still permits mobile computing [60] and (iii) its depth does not require large numbers of samples per class to prevent overfitting [61]. A ResNet-50 pre-trained with ImageNet data consisting of (1.28 million training images belongs to 1000 object classes) was used to greatly reduce the sample size (muzzle image) requirements by Keras with TensorFlow backend [56].…”

Section: Biometric Model Trainingmentioning

confidence: 99%

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

et al. 2021

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Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these initial findings into a practical precision livestock monitoring system, which can be deployed at scale for large herds. The objective of this study was to investigate and address key limitations to the autonomous biometric identification of cattle. The contributions of this work are fourfold: (1) provision of a large publicly-available dataset of cattle face images (300 individual cattle) to facilitate further research in this field, (2) development of a two-stage YOLOv3-ResNet50 algorithm that first detects and extracts the cattle muzzle region in images and then applies deep transfer learning for biometric identification, (3) evaluation of model performance across a range of cattle breeds, and (4) utilizing few-shot learning (five images per individual) to greatly reduce both the data collection requirements and duration of model training. Results indicated excellent model performance. Muzzle detection accuracy was 99.13% (1024 × 1024 image resolution) and biometric identification achieved 99.11% testing accuracy. Overall, the two-stage YOLOv3-ResNet50 algorithm proposed has substantial potential to form the foundation of a highly accurate automated cattle biometric identification system, which is applicable in livestock farming systems. The obtained results indicate that utilizing livestock biometric monitoring in an advanced manner for resource management at multiple scales of production is possible for future agriculture decision support systems, including providing useful information to forecast acceptable stocking rates of pastures.

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Section: Biometric Model Trainingmentioning

confidence: 99%

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

et al. 2021

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“…Deep neural networks (DNN) are powerful algorithms that can, with appropriate computing power, be applied to large images such as H&E-stained WSIs of tissue derived from biopsies or surgical resections. These model architectures have indeed excelled at classification of images such as determining whether a digitized stained slide contains cancer cells or not (2,3,(7)(8)(9)(10)(11)(12)(13). While attaining highest prediction accuracies for distinguishing tumor from healthy cells (AUCs > 0.99), DNNs are used for more challenging classification tasks as well, such as distinguishing between closely related cancer subtypes (such as adenocarcinoma vs. adenoma in gastric and colon cancers and adenocarcinoma vs. squamous cell carcinoma in lung tumors) and detecting benign versus malignant tissue.…”

Section: Making Cancer Diagnoses More Accuratementioning

confidence: 99%

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder

Gilvary²,

Madhukar³

et al. 2021

Cancer Discovery

236

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Convolutional neural networks (CNN) have been the most popular deep learning architectures used for image classification in cancer ( Fig. 1 ). CNNs apply a series of nonlinear transformations to structured data (such as raw pixels of an image) to learn relevant features automatically, unlike conventional machine learning models that frequently require manual feature curation. On the fl ip side, it is diffi cult to tell what features are learned by the CNNs, making them what many have referred to as a "black box." One consequence is that images used for CNNs should be carefully prepro cessed to reduce the risk that the model learns from image artifacts. There are two major approaches for CNN models; one is transfer learning that uses images from a large collection of natural objects (such as in ImageNet) to train the initial layers of a model (where the model learns to identify general features such as shapes and edges) and then uses the diseasespecifi c data to fi ne-tune the training parameters in the last layers. The second variation of CNN is based on an autoencoder where the model learns background features from a subset of representative images and encodes a compressed representation of the basic features later used to initialize the CNN. In the CAMELYON16 Challenge-a crowdsourced competition to identify and classify lymph node metastasis in patients with breast cancer from whole slide images (WSI) of hematoxylin and eosin (H&E)-stained tumors-25 of the 32 submitted algorithms were CNNs, and the top 5 classifi cation models were exclusively based on transfer learning, which were GoogLeNet, ResNet, VGG-16 ( 2 ). Khosravi and colleagues trained and tested several state-of-the-art deep learning models to classify WSI from H&E-stained tumor tissues of The Cancer Genome Atlas (TCGA) cohort and reported on the

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“…ResNet network has shown high accuracy on image classification tasks. 17 Qasem et al 18 reported an accuracy rate of up to 99.1% using the ResNet-50 model. Mahesh et al 19 designed a residual learning-based 152-layered convolutional neural network, named ResHist for breast cancer histopathological image classification, and an accuracy of 92.52% was achieved when data augmentation is employed.…”

Section: Introductionmentioning

confidence: 99%

“…In that paper, several models were compared, and the results indicated that there was no significant difference by using ResNet-152 and ResNet-50. Due to the imbalance of the sample, data augmentation technology was adopted by researchers, [18][19][20] and proved to be effective. However, most of them applied traditional transformation methods, without considering the characteristics of pathological images.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, transfer learning technology was also used to improve the performance of the model. 15,18,20 In this study, a novel deep learning-based framework was proposed for breast cancer diagnosis, including stain normalization, data augmentation, and deep learningbased model design, in order to improve the classification results. The main contribution of this paper is summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

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Classification of breast cancer histopathological image with deep residual learning

Sun

Yuan

et al. 2021

Int J Imaging Syst Tech

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Breast cancer has high incidences and mortality rates in women worldwide.Malignancy could be detected manually by experienced pathologists based on Hematoxylin and Eosin (H&E) stained images. However, it is time-consuming and experience-dependent, making early diagnosis a big challenge. In this paper, a methodology for breast cancer classification based on histopathological images with deep learning was described. A residual learning-based convolutional neural network named myResNet-34 was designed for malignancy-and-benign classification. In addition, an algorithm automatically generating the target image for stain normalization was proposed, which eliminated the bias caused by manual selection of the reference image. Elastic distortion was introduced and combined with affine transformation for data augmentation considering the characteristics of the H&E images. Experiments were conducted on BreakHis dataset with the proposed framework. Promising results were achieved with an average classification accuracy of around 91% on image-level classification. Results indicated that both our data augmentation and stain normalization effectively improved the classification accuracy by 2-3%.

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Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network

Cited by 91 publications

References 12 publications

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

Artificial Intelligence in Cancer Research and Precision Medicine

Classification of breast cancer histopathological image with deep residual learning

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