[Retracted] Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

Reshma, V. K.; Arya, Nancy; Ahmad, Sayed Sayeed; Wattar, Ihab; Mekala, Sreenivas; Joshi, Shubham; Krah, Daniel

doi:10.1155/2022/8363850

Cited by 35 publications

(13 citation statements)

References 32 publications

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“…Figure 7 displays the comparative evaluation of the obtained accuracy analysis between the proposed system with recent algorithms since this metric is standard in all conducted works. This graph shows that the implemented methods in [ 4 , 10 ] achieved minimal accuracies of 81% and 92.44%, respectively. Furthermore, the implemented method in [ 8 ] achieved a moderate accuracy of 96.5%.…”

Section: Resultsmentioning

confidence: 99%

“… The obtained accuracy analysis charts between the proposed system and some related work [ 1 , 2 , 3 , 4 ] and [ 7 , 8 , 9 , 10 ]. …”

Section: Figurementioning

confidence: 98%

“…This name was given because the cancer starts and develops inside the milk ducts; 90% of reported breast cancer cases are diagnosed with this type [ 2 , 3 , 4 , 5 , 6 , 7 ]. In most cases, patients feel no pain, and no symptoms appear; however, symptoms and signs can appear in advanced stages, when it could be too late to treat [ 3 , 5 , 6 , 7 , 8 , 9 , 10 ]. These symptoms can be changes in shape, size, appearance, a newly inverted nipple, breast tenderness, and itching.…”

Section: Introductionmentioning

confidence: 99%

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Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers

et al. 2022

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Breast cancer is considered one of the deadliest diseases in women. Due to the risk and threat it poses, the world has agreed to hold a breast cancer awareness day in October, encouraging women to perform mammogram inspections. This inspection may prevent breast-cancer-related deaths or reduce the death rate. The identification and classification of breast cancer are challenging tasks. The most commonly known procedure of breast cancer detection is performed by using mammographic images. Recently implemented algorithms suffer from generating accuracy below expectations, and their computational complexity is high. To resolve these issues, this paper proposes a fully automated biomedical diagnosis system of breast cancer using an AlexNet, a type of Convolutional Neural Network (CNN), and multiple classifiers to identify and classify breast cancer. This system utilizes a neuro-fuzzy method, a segmentation algorithm, and various classifiers to reach a higher accuracy than other systems have achieved. Numerous features are extracted to detect and categorize breast cancer. Three datasets from Kaggle were tested to validate the proposed system. The performance evaluation is performed with quantitative and qualitative accuracy, precision, recall, specificity, and F-score. In addition, a comparative assessment is performed between the proposed system and some works of literature. This assessment shows that the presented algorithm provides better classification results and outperforms other systems in all parameters. Its average accuracy is over 98.6%, while other metrics are more than 98%. This research indicates that this approach can be applied to assist doctors in diagnosing breast cancer correctly.

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Section: Resultsmentioning

confidence: 99%

“… The obtained accuracy analysis charts between the proposed system and some related work [ 1 , 2 , 3 , 4 ] and [ 7 , 8 , 9 , 10 ]. …”

Section: Figurementioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers

et al. 2022

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“…To highlight the enhanced outcomes of the CSSADTL-BCC model, a brief comparison study with recent models is shown in Table 3 [ 22 ]. Figure 11 investigates a detailed

and

analysis of the CSSADTL-BCC with existing models.…”

Section: Performance Validationmentioning

confidence: 99%

Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images

Shankar

Dutta

Kumar

et al. 2022

Cancers

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Breast cancer is the major cause behind the death of women worldwide and is responsible for several deaths each year. Even though there are several means to identify breast cancer, histopathological diagnosis is now considered the gold standard in the diagnosis of cancer. However, the difficulty of histopathological image and the rapid rise in workload render this process time-consuming, and the outcomes might be subjected to pathologists’ subjectivity. Hence, the development of a precise and automatic histopathological image analysis method is essential for the field. Recently, the deep learning method for breast cancer pathological image classification has made significant progress, which has become mainstream in this field. This study introduces a novel chaotic sparrow search algorithm with a deep transfer learning-enabled breast cancer classification (CSSADTL-BCC) model on histopathological images. The presented CSSADTL-BCC model mainly focused on the recognition and classification of breast cancer. To accomplish this, the CSSADTL-BCC model primarily applies the Gaussian filtering (GF) approach to eradicate the occurrence of noise. In addition, a MixNet-based feature extraction model is employed to generate a useful set of feature vectors. Moreover, a stacked gated recurrent unit (SGRU) classification approach is exploited to allot class labels. Furthermore, CSSA is applied to optimally modify the hyperparameters involved in the SGRU model. None of the earlier works have utilized the hyperparameter-tuned SGRU model for breast cancer classification on HIs. The design of the CSSA for optimal hyperparameter tuning of the SGRU model demonstrates the novelty of the work. The performance validation of the CSSADTL-BCC model is tested by a benchmark dataset, and the results reported the superior execution of the CSSADTL-BCC model over recent state-of-the-art approaches.

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“…Detection is finetuned after pretraining using ImageNet. en, there is the question of whether this kind of backbone can be utilized to extract image features for target identification to maximize performance [10,[23][24][25][26]. e expense of reaching exceptional detection performance, on the other hand, would be rather significant if a new backbone was developed and pretrained on ImageNet.…”

Section: Relu (Cmentioning

confidence: 99%

Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network

Mohanakurup

Gangadharan

Goel

et al. 2022

Computational Intelligence and Neuroscience

144

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Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDBN uses the lead backbone feature maps to identify objects. It feeds high-level output features from previous backbones into the next backbone in a stepwise way. We show that most contemporary detectors can easily include CDBN to improve performance achieved mAP improvements ranging from 1.5 to 3.0 percent on the breast cancer histopathological image classification (BreakHis) dataset. Experiments have also shown that instance segmentation may be improved. In the BreakHis dataset, CDBN enhances the baseline detector cascade mask R-CNN (mAP = 53.3). The proposed CDBN detector does not need pretraining. It creates high-level traits by combining low-level elements. This network is made up of several identical backbones that are linked together. The composite dilated backbone considers the linked backbones CDBN.

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[Retracted] Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques

Cited by 35 publications

References 32 publications

Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers

Biomedical Diagnosis of Breast Cancer Using Deep Learning and Multiple Classifiers

Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images

Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network

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