Application of Deep Learning to Construct Breast Cancer Diagnosis Model

Lin, Rong-Ho; Kujabi, Benjamin Kofi; Chuang, Chun‐Ling; Lin, Chun-Ming; Chiu, Chun-Jen

doi:10.3390/app12041957

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…Their ( 18 ) goal was to create a hierarchical breast cancer system model that would improve detection accuracy and reduce breast cancer misdiagnosis. To categorize breast cancer tumors and compare their performances, the dataset was subjected to ANN and SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

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Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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“…This technique improves the information about breast mass. (Ull et al, 2023;Bandaru et al, 2022a;Duong et al, 2023;Kahnouei et al, 2022;P and V, 2022;Li et al, 2019aLi et al, , 2021cMahmood et al, 2022;Wang et al, 2022;Yao et al, 2022), (Tsochatzidis et al, 2021;Ahmed et al, 2020;Fathy and Ghoneim, 2019;Frazer et al, 2021;Sannasi Chakravarthy and Rajaguru, 2021;Chakravarthy and Rajaguru, 2022;Al-Antari et al, 2020;Al-Mansour et al, 2022;Alruwaili and Gouda, 2022;Altameem et al, 2022;Altaf, 2021;Cao et al, 2020;Al-Tam et al, 2022;Bandaru et al, 2022b;López-Cabrera et al, 2020;Saber et al, 2021;Chougrad et al, 2018;Falconi et al, 2020;Jafarzadeh Ghoushchi et al, 2021;Hanis et al, 2023;Lin et al, 2022;Mokni and Haoues, 2022;Mudeng et al, 2022;Khan and Masala, 2023;Oza et al, 2023;Prodan et al, 2023;Ragab et al, 2021;Yu et al, 2023b;Gerbasi et al, 2023;Shanker and Vadivel, 2022;Zhang and Wang, 2019;Adedigba et al, 2022;…”

Section: Techniques References Descriptionmentioning

confidence: 99%

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

Amin,

Acharya,

et al. 2024

Preprint

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Breast cancer (BC) is a leading cause of mortality in women worldwide, and its incidence and prognosis vary greatly, especially in low-income areas such as Asia and Africa, compared to America and Europe. This review examines the critical role of computer-aided diagnostic systems utilizing Deep Learning (DL) techniques in improving the precision of BC detection, which can help researchers and practitioners better understand the obstacles and emerging trends in the field. The comprehensive analysis and synthesis of the published articles is provided by the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Following several searches, 287 articles were found eligible for the assessment followed by data extraction and synthesis. This work provides insight into the pathology of BC and its corresponding mammographic appearance, a comprehensive analysis of publication trends, important contributors, and emerging themes using bibliometric techniques in addition to a thorough review of research articles. Focus maps were also created to identify and shed light on the body of knowledge. In contrast to other research, this review sheds light on the subtle identification of breast density, mass, and calcification from mammography images by emphasizing the critical evaluation of image pre- processing, augmentation, detection, segmentation, and classification techniques used from 2018 to 2023, further highlighting articles related to the Asian dataset. Furthermore, the review examines contributions made by authors, countries, and experimental datasets, providing insights into various approaches. Prospero registration: CRD42022478896

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“…Further, Lin et al [134] developed a DL model for breast cancer diagnosis. Data classification was carried out using ANN and SVM.…”

Section: Deep Learningmentioning

confidence: 99%

Breast Cancer Detection using Mammography: Image Processing to Deep Learning

Qureshi,

Rehman,

Hussain

et al. 2024

Preprint

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Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

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Application of Deep Learning to Construct Breast Cancer Diagnosis Model

Cited by 12 publications

References 39 publications

Breast Cancer Detection and Classification Empowered With Transfer Learning

Breast Cancer Detection and Classification Empowered With Transfer Learning

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

Breast Cancer Detection using Mammography: Image Processing to Deep Learning

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