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 8 publications

(3 citation statements)

References 35 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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

show abstract

“…As an important branch of AI, deep learning technology, which has made significant progress since 2012, has been widely used in image classification (He et al, 2020), lesion detection, and segmentation (Ren et al, 2017;Falk et al, 2019). In some respects, it has reached or exceeded the diagnostic level of clinicians, as is the case in the diagnosis and prognosis evaluation of hepatobiliary malignant tumors (Ibragimov et al, 2018;Zhou et al, 2019), the early diagnosis and pathological classification prediction of lung cancer (Coudray et al, 2018), the establishment of a breast cancer diagnosis model to predict malignant breast cancer (Li, 2021), and the automatic detection of fundus images identifying glaucoma and diabetic retinopathy (Schmidt-Erfurth et al, 2018;Ting et al, 2019). Despite these technological advancements, few mature studies exist on the AI-aided diagnosis of smaller structures, e.g., the auditory ossicles.…”

mentioning

confidence: 99%

Application value of a deep learning method based on a 3D V-Net convolutional neural network in the recognition and segmentation of the auditory ossicles

Wang

Jin

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

ObjectiveTo explore the feasibility of a deep learning three-dimensional (3D) V-Net convolutional neural network to construct high-resolution computed tomography (HRCT)-based auditory ossicle structure recognition and segmentation models.MethodsThe temporal bone HRCT images of 158 patients were collected retrospectively, and the malleus, incus, and stapes were manually segmented. The 3D V-Net and U-Net convolutional neural networks were selected as the deep learning methods for segmenting the auditory ossicles. The temporal bone images were randomized into a training set (126 cases), a test set (16 cases), and a validation set (16 cases). Taking the results of manual segmentation as a control, the segmentation results of each model were compared.ResultsThe Dice similarity coefficients (DSCs) of the malleus, incus, and stapes, which were automatically segmented with a 3D V-Net convolutional neural network and manually segmented from the HRCT images, were 0.920 ± 0.014, 0.925 ± 0.014, and 0.835 ± 0.035, respectively. The average surface distance (ASD) was 0.257 ± 0.054, 0.236 ± 0.047, and 0.258 ± 0.077, respectively. The Hausdorff distance (HD) 95 was 1.016 ± 0.080, 1.000 ± 0.000, and 1.027 ± 0.102, respectively. The DSCs of the malleus, incus, and stapes, which were automatically segmented using the 3D U-Net convolutional neural network and manually segmented from the HRCT images, were 0.876 ± 0.025, 0.889 ± 0.023, and 0.758 ± 0.044, respectively. The ASD was 0.439 ± 0.208, 0.361 ± 0.077, and 0.433 ± 0.108, respectively. The HD 95 was 1.361 ± 0.872, 1.174 ± 0.350, and 1.455 ± 0.618, respectively. As these results demonstrated, there was a statistically significant difference between the two groups (P < 0.001).ConclusionThe 3D V-Net convolutional neural network yielded automatic recognition and segmentation of the auditory ossicles and produced similar accuracy to manual segmentation results.

show abstract

Application of Deep Learning to Construct Breast Cancer Diagnosis Model

Cited by 8 publications

References 35 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

Application value of a deep learning method based on a 3D V-Net convolutional neural network in the recognition and segmentation of the auditory ossicles

Contact Info

Product

Resources

About