Deep-learning based breast cancer detection for cross-staining histopathology images

Huang, P. C.; Ouyang, Hsu; Hsu, Bang-Yi; Chang, Yu-Ruei; Lin, Yu-Chieh; Chen, Yung-An; Hsieh, Yu-Han; Fu, Chien-Chung; Li, Chien‐Feng; Lin, Ching‐Hung; Lin, Yen-Yin; Chang, Margaret Dah‐Tsyr; Pai, Tun-Wen

doi:10.1016/j.heliyon.2023.e13171

Cited by 10 publications

(1 citation statement)

References 44 publications

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“…Artificial intelligence (AI) algorithms can serve as appropriate models to assist physicians in diagnosing breast cancer and categorizing patients. The integration of AI in the field of medical science is of paramount importance for improving the accuracy and performance of disease diagnosis (4)(5)(6). Promising reports on machine learning (ML) methods and intelligent techniques in breast cancer diagnosis research indicate their potential to enhance diagnostic performance (7,8).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

Dehghan,

Azizi

2023

Jundishapur J Nat Pharm Prod

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Background: Breast cancer is the second leading cause of death in women. The advent of machine learning (ML) has opened up a world of possibilities for the discovery and formulation of drugs. It is an exciting development that could revolutionize the pharmaceutical industry. By leveraging ML algorithms, researchers can now identify disease-related targets with greater accuracy. Additionally, ML techniques can be used to predict the toxicity and pharmacokinetics of potential drug candidates. Objectives: The main purpose of ML techniques, such as feature selection (FS) and classification, is to develop a learning model based on datasets. Methods: This paper proposed a hybrid intelligent approach using a Binary Grey Wolf Optimization Algorithm and a Self-Organizing Fuzzy Logic Classifier (BGWO-SOF) for breast cancer diagnosis. The proposed FS approach can not only reduce the complexity of feature space but can also avoid overfitting and improve the learning process. The performance of this proposed approach was evaluated on the 10-fold cross-validation technique and the Wisconsin Diagnostic Breast Cancer dataset. Although the performance of breast cancer detection is highly dependent on classification accuracy, most good classification methods have an essential flaw in that they simply seek to maximize the accuracy of classification while ignoring the costs of misclassification among various categories. This is even more important in classification problems when the initial set of features is large. With such a large number of features, it is of special interest to search for a dependency between an optimal number of selected features and the accuracy of the classification model. Results: In experiments, standard performance evaluation metrics, including accuracy, F-measure, precision, sensitivity, and specificity, were performed. The evaluation results demonstrated that the BGWO-SOF approach achieves 99.70% accuracy and 99.66% F-measure, which outperforms other state-of-the-art methods. Conclusions: During the comparison of the results, it was observed that the proposed approach gives better or more competitive results than other state-of-the-art methods. By leveraging the power of ML algorithms and artificial intelligence (AI) and the findings of the current study, we can optimize the selection of natural pharmaceutical products for the treatment of breast cancer and maximize their efficacy.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

Dehghan,

Azizi

2023

Jundishapur J Nat Pharm Prod

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For the Nuclei Segmentation of Liver Cancer Histopathology Images, A Deep Learning Detection Approach is Used

Arifullah,

Chakir,

Sebai

et al. 2024

Synthesis Lectures on Engineering, Science, and Technology

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Advancing Autonomous Nanomedicine: Bridging the Gap from Concept to Potential Clinical Studies

Chohan,

Dey,

Tarkunde

et al. 2024

J Clust Sci

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Autonomous nanomedicine, a burgeoning field within nanotechnology and biomedical sciences, is poised to revolutionize healthcare by eliminating the need for external intervention in targeted applications within the body. This article elucidates the promise and challenges of autonomous nanomedicine, emphasizing its ability to overcome the limitations of traditional methods such as chemotherapy and radiotherapy. Central to its efficacy are nano-sized carriers, which autonomously navigate the body to deliver therapeutic agents with precision and control. By integrating automated nanoscale tools into disease detection processes, this technology offers swift and personalized assessments, reshaping disease management paradigms. To advance the clinical translation of autonomous nanomedicine, rigorous preclinical studies are imperative. However, challenges persist in ensuring reproducibility and safety, hindering progress in clinical trials. This article examines current studies with potential clinical translation, shedding light on the regulatory and ethical considerations crucial for its safe implementation. As the field progresses, maintaining a balance between innovation and safety remains paramount for harnessing the full potential of autonomous nanomedicine while safeguarding patient well-being. Graphical Abstract

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Deep-learning based breast cancer detection for cross-staining histopathology images

Cited by 10 publications

References 44 publications

A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

For the Nuclei Segmentation of Liver Cancer Histopathology Images, A Deep Learning Detection Approach is Used

Advancing Autonomous Nanomedicine: Bridging the Gap from Concept to Potential Clinical Studies

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