A novel deep learning based framework for the detection and classification of breast cancer using transfer learning

Khan, Sana Ullah; Islam, Naveed; Jan, Zahoor; Din, Ikram Ud; Rodrigues, Joel J. P. C.

doi:10.1016/j.patrec.2019.03.022

Cited by 575 publications

(243 citation statements)

References 12 publications

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“…CNN have achieved state-of-the-art recognition accuracies in many classification problems including plant disease detection [13], cancer [14,15], and skin burns assessment [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

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While visual assessment is the standard technique for burn evaluation, computer-aided diagnosis is increasingly sought due to high number of incidences globally. Patients are increasingly facing challenges which are not limited to shortage of experienced clinicians, lack of accessibility to healthcare facilities, and high diagnostic cost. Certain number of studies were proposed in discriminating burn and healthy skin using machine learning leaving a huge and important gap unaddressed; whether burns and related skin injuries can be effectively discriminated using machine learning techniques. Therefore, we specifically use pre-trained deep learning models due to deficient dataset to train a new model from scratch. Experiments were extensively conducted using three stateof-the-art pre-trained deep learning models that includes ResNet50, ResNet101 and ResNet152 for image patterns extraction via two transfer learning strategies: fine-tuning approach where dense and classification layers were modified and trained with features extracted by base layers, and in the second approach support vector machine (SVM) was used to replace top-layers of the pre-trained models, trained using off-the-shelf features from the base layers. Our proposed approach records near perfect classification accuracy of approximately 99.9%.

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“…CNN have achieved state-of-the-art recognition accuracies in many classification problems including plant disease detection [13], cancer [14,15], and skin burns assessment [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Abubakar¹,

Ajuji²,

Yahya³

2020

Preprint

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“…In this method a large dataset from a source task is employed for training of a target task using the weights trained by the images from source dataset. The main advantage of transfer learning is the improvement of classifier accuracy and the acceleration of the learning process [33]. Previous studies in the literature have demonstrated that transfer learning also has the potential to reduce the problem of overfitting [34] [35].…”

Section: Pre-trained Dcnn Feature Extractorsmentioning

confidence: 99%

Breast Cancer Diagnosis with Transfer Learning and Global Pooling

Kassani

Wesolowski

et al. 2019

2019 International Conference on Information and Communication Technology Convergence (ICTC)

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Breast cancer is one of the most common causes of cancer-related death in women worldwide. Early and accurate diagnosis of breast cancer may significantly increase the survival rate of patients. In this study, we aim to develop a fully automatic, deep learning-based, method using descriptor features extracted by Deep Convolutional Neural Network (DCNN) models and pooling operation for the classification of hematoxylin and eosin stain (H&E) histological breast cancer images provided as a part of the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology (BACH) Images. Different data augmentation methods are applied to optimize the DCNN performance. We also investigated the efficacy of different stain normalization methods as a pre-processing step. The proposed network architecture using a pre-trained Xception model yields 92.50% average classification accuracy.

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“…These consider a linear combination of covariates to predict the risk of the patient's death with nonlinear functions related to the risk [27]. Another group is based on artificial intelligence and deep learning, on which deep convolutional neural networks (DCNN) are used for the analysis of biomedical imaging and applied to recognition, classification and prediction tasks [28][29][30][31]. Numerous examples that use DCNNs have been reported recently to predict the survival rate based on pathological images including Katzman et al [32] who put forwards for the first time deep fully connected network, namely, DeepSurv, to predict survival rate based on structured clinical data (non-images data) and Zhu et al [27] who used a modified DCNN, namely, DeepConvSurv, on the unstructured data (867 lung cancer WSIs pathological images) to predict the survival rate.…”

Section: Introductionmentioning

confidence: 99%

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

Shirazi

Fornaciari

Bagherian

et al. 2020

Med Biol Eng Comput

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Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients' survival, which can help in scheduling patients therapeutic treatment and allocate time for preclinical studies to guide personalized treatments. We now propose a new classifier, namely, DeepSurvNet powered by deep convolutional neural networks, to accurately classify in 4 classes brain cancer patients' survival rate based on histopathological images (class I, 0-6 months; class II, 6-12 months; class III, 12-24 months; and class IV, >24 months survival after diagnosis). After training and testing of DeepSurvNet model on a public brain cancer dataset, The Cancer Genome Atlas, we have generalized it using independent testing on unseen samples. Using DeepSurvNet, we obtained precisions of 0.99 and 0.8 in the testing phases on the mentioned datasets, respectively, which shows DeepSurvNet is a reliable classifier for brain cancer patients' survival rate classification based on histopathological images. Finally, analysis of the frequency of mutations revealed differences in terms of frequency and type of genes associated to each class, supporting the idea of a different genetic fingerprint associated to patient survival. We conclude that DeepSurvNet constitutes a new artificial intelligence tool to assess the survival rate in brain cancer.

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A novel deep learning based framework for the detection and classification of breast cancer using transfer learning

Cited by 575 publications

References 12 publications

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination

Breast Cancer Diagnosis with Transfer Learning and Global Pooling

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

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