Breast cancer masses classification using deep convolutional neural networks and transfer learning

Hassan, Shayma'a A.; Sayed, Mohammed S.; Abdalla, Mahmoud I.; Rashwan, Mohsen

doi:10.1007/s11042-020-09518-w

Cited by 64 publications

(30 citation statements)

References 31 publications

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“…The yielded results in this research using the AlexNet have surpassed the recent results achieved in the literature. The overall accuracy of the AlexNet, GoogLeNet on the MIAS Dataset achieved in [42], and [9] was 95.70%, 91.58% respectively. And as depicted in Table I, the conventional machine learning approached has yielded an extraordinary result that is greater than the results achieved the pretrained CNN networks.…”

Section: Resultsmentioning

confidence: 98%

“…This is a fact that should be considered when we select an algorithm for cancer detection, classical or deep learning-based method, for a CAD system. Currently, there are a huge number of researches [5] [8] [9] that have utilized the deep learning approaches and recommending them as they have yielded higher accuracies, above 97%. However, a comparable accuracy, 96%, has been attained using the conventional machine learning paradigms as surveyed in [10].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

Alhussan¹,

Samee²,

Ghoneim³

et al. 2021

IJACSA

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

Alhussan¹,

Samee²,

Ghoneim³

et al. 2021

IJACSA

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“…A semi-supervised DCNN was developed [10] to improve the accuracy of diagnosing BC for a large amount of unlabeled data. Diving and cotraining processes were incorporated in the DL algorithms for analyzing the cancer images.…”

Section: Literature Surveymentioning

confidence: 99%

Breast Cancer Detection in Mammogram Images by MapReduce Based Deep Convolutional Neural Networks

2022

IJIES

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Imaging techniques using mammographic pictures are the most efficient and straightforward way to diagnose Breast Cancer (BC). The accurate discovery can significantly lower the mortality rate caused by BC. Machine Learning (ML) techniques were utilized for the prediction of BC in images. The ML approaches offered a unique way of making decisions, and they were able to aid the medical expert in providing a second perspective on more precise nodule detection. However, the over-fitting problem in ML degraded the performance of BC detection. To solve this issue, Deep Learning (DL) algorithms were utilized to detect BC early using mammographic images, which is a huge help in detecting BC at an early stage. The Deep Convolutional Neural Networks (DCNN) are being used in DL to help with an accurate BC diagnosis and better BC image outcome detection. However, the time complexity of DCNN is high for a large-scale dataset. In this paper, The DCNN is parallelized in the Mappers of MapReduce (MR) programming model in which the network weights can be iteratively adjusted via determining their fractional gradients following all collections of learning images are propagated throughout the system. Accordingly, the Mapper will parallelize the training phase by distributing the normal, benign, and malignant images that have been preprocessed using the K-Means (KM) algorithm. Each image can be provided to various DCNNs and each DCNN is trained independently in parallel. The output weight of the training stage of DCNNs is aggregated in Reducer and then the weights are then updated for the next iteration. After the completion of the training process, the updated weight is utilized in the all distributed DCNN for classifying the test images of BC. The whole process is termed MapReduce-KM-DCNN (MR-KM-DCNN). Finally, the experimental outcome demonstrates that the proposed MR-KM-DCNN method achieves 88.35 percent accuracy, which is 13.48%, 8.02 %, and 4.85 % higher than the existing SSL-DCNN, AN-DCNN, and KM-DCNN methods. In addition, the running time is reduced by 60% on average compared to previous methods. As a result, it has been verified that the MR-KM-DCNN attained the highest BC detection rate while requiring the smallest amount of computational time.

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“…In [18], the authors proposed the use of deep-learning techniques for the diagnosis of malaria diseases. This was used basically to discriminate a blood-smear sample that contains malaria and those samples that are negative.…”

Section: Literature Reviewmentioning

confidence: 99%

DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Abubakar

Ajuji

Yahya

2021

ASI

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Malaria is one of the most infectious diseases in the world, particularly in developing continents such as Africa and Asia. Due to the high number of cases and lack of sufficient diagnostic facilities and experienced medical personnel, there is a need for advanced diagnostic procedures to complement existing methods. For this reason, this study proposes the use of machine-learning models to detect the malaria parasite in blood-smear images. Six different features—VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models—were extracted. Then Decision Tree, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour classifiers were trained using these six features. Extensive performance analysis is presented in terms of precision, recall, f-1score, accuracy, and computational time. The results showed that automating the process can effectively detect the malaria parasite in blood samples with an accuracy of over 94% with less complexity than the previous approaches found in the literature.

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Breast cancer masses classification using deep convolutional neural networks and transfer learning

Cited by 64 publications

References 31 publications

Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

Breast Cancer Detection in Mammogram Images by MapReduce Based Deep Convolutional Neural Networks

DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

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