A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

Saber, Abeer; Sakr, Mohamed; Abo-Seida, Osama M.; Keshk, Arabi; Chen, Huiling

doi:10.1109/access.2021.3079204

Cited by 227 publications

(100 citation statements)

References 113 publications

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“…Owing to its strong optimization capability, the proposed GOMGBO can also be applied to other optimization problems, such as regression tasks, 141 medical diagnosis, [142][143][144][145] covert communication systems, [146][147][148] service ecosystem, 149,150 image editing, [151][152][153] energy storage planning and scheduling, 154 social recommendation and quality-of-service (QoS)-aware service composition, [155][156][157] active surveillance, 158 pedestrian dead reckoning, 159 evaluation of human lower limb motions, 160 image super resolution, [161][162][163] sentiment classification, 164 data-to-text generation, 165 crowd sensing, 166 and feature selection. [167][168][169]…”

Section: Experimental Results Of the Mammographic Data Setmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

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Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

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Section: Experimental Results Of the Mammographic Data Setmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed approach outperforms (Kashif, 2020) in the precision of abnormal, the recall of normal, and F1-score of abnormal while (Kashif, 2020) exceeds our approach in the accuracy. The proposed approach exceeds (Saber et al, 2021) only on the recall of the normal. While (Yu et al, 2020) outperforms our approach in terms of accuracy only.…”

Section: (A)comparisonmentioning

confidence: 89%

“…In (Saber et al, 2021), the authors have applied a 2D median filter with a kernel size of 3 � 3 to aid in removing the noise from the images of the MIAS dataset. Classical histogram equalization has been applied in order to strengthen the contrast of the original image to make the image anomalies more visible.…”

Section: Literature Reviewmentioning

confidence: 99%

Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

et al. 2021

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Breast cancer constitutes a significant threat to women's health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification. The first classification stage which distinguishes

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“…Due to its strong optimization capability, the developed MSMA can also be applied to other optimization problems, such as multi-objective or many optimization problems [75][76][77], big data optimization problems [78], and combination optimization problems [79]. Moreover, it can be applied to tackle the practical problems such as medical diagnosis [80][81][82][83], location-based service [84,85], service ecosystem [86], communication system conversion [87][88][89], kayak cycle phase segmentation [90], image dehazing and retrieval [91,92], information retrieval service [93][94][95], multi-view learning [96], human motion capture [97], green supplier selection [98], scheduling [99][100][101], and microgrid planning [102] problems.…”

Section: Discussionmentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

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In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

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A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

Cited by 227 publications

References 113 publications

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

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