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Oztekin PS, Katar O, Omma T, Erel S, Tokur O, Avci D, Aydogan M, Yildirim O, Avci E, Acharya UR. Comparison of Explainable Artificial Intelligence Model and Radiologist Review Performances to Detect Breast Cancer in 752 Patients. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024. [PMID: 39051752 DOI: 10.1002/jum.16535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/11/2024] [Accepted: 07/13/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVES Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis. METHODS In this study, we proposed an artificial intelligence (AI)-based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast-XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022. RESULTS Six classifiers, support vector machine (SVM), K-nearest neighbor (K-NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast-XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (X2GAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model. CONCLUSIONS The results obtained by radiologists and the X2GAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results.
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Affiliation(s)
- Pelin Seher Oztekin
- Department of Radiology, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey
| | - Oguzhan Katar
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Tulay Omma
- Department of Endocrinology and Metabolism, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey
| | - Serap Erel
- Department of Surgery, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey
| | - Oguzhan Tokur
- Department of Radiology, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey
| | - Derya Avci
- Department of Computer Technology, Firat University, Elazig, Turkey
| | - Murat Aydogan
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Ozal Yildirim
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Engin Avci
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Queensland, Australia
- Centre for Health Research, University of Southern Queensland, Springfield, Queensland, Australia
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Sannasi Chakravarthy SR, Bharanidharan N, Vinoth Kumar V, Mahesh TR, Alqahtani MS, Guluwadi S. Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer. BMC Med Imaging 2024; 24:82. [PMID: 38589813 DOI: 10.1186/s12880-024-01267-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 03/30/2024] [Indexed: 04/10/2024] Open
Abstract
Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.
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Affiliation(s)
- S R Sannasi Chakravarthy
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India
| | - N Bharanidharan
- School of Computer Science Engineering and Information systems, Vellore Institute of Technology, Vellore, 632014, India
| | - V Vinoth Kumar
- School of Computer Science Engineering and Information systems, Vellore Institute of Technology, Vellore, 632014, India
| | - T R Mahesh
- Department of Computer Science and Engineering JAIN (Deemed-to-be University), Bengaluru, 562112, India
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, 61421, Saudi Arabia
| | - Suresh Guluwadi
- Adama Science and Technology University, Adama, 302120, Ethiopia.
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Kebede SR, Waldamichael FG, Debelee TG, Aleme M, Bedane W, Mezgebu B, Merga ZC. Dual view deep learning for enhanced breast cancer screening using mammography. Sci Rep 2024; 14:3839. [PMID: 38360869 PMCID: PMC10869685 DOI: 10.1038/s41598-023-50797-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/26/2023] [Indexed: 02/17/2024] Open
Abstract
Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of EfficientNet-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.
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Affiliation(s)
- Samuel Rahimeto Kebede
- Research Development Cluster, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia.
- College of Engineering, Debre Berhan University, Debre Berhan, Ethiopia.
| | - Fraol Gelana Waldamichael
- Research Development Cluster, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia
| | - Taye Girma Debelee
- Research Development Cluster, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa, 120611, Ethiopia
| | | | - Wubalem Bedane
- Radiology, St. Pauli Millenium Medical College, Addis Ababa, Ethiopia
| | - Bethelhem Mezgebu
- Radiology, St. Pauli Millenium Medical College, Addis Ababa, Ethiopia
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4
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Cho H, She J, De Marchi D, El-Zaatari H, Barnes EL, Kahkoska AR, Kosorok MR, Virkud AV. Machine Learning and Health Science Research: Tutorial. J Med Internet Res 2024; 26:e50890. [PMID: 38289657 PMCID: PMC10865203 DOI: 10.2196/50890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 11/30/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024] Open
Abstract
Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.
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Affiliation(s)
- Hunyong Cho
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jane She
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Daniel De Marchi
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Helal El-Zaatari
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Edward L Barnes
- Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Center for Gastrointestinal Biology and Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arti V Virkud
- Kidney Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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5
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Zhang L, Xiao Z, Jiang W, Luo C, Ye M, Yue G, Chen Z, Ouyang S, Liu Y. Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing. Health Inf Sci Syst 2023; 11:51. [PMID: 37954065 PMCID: PMC10632346 DOI: 10.1007/s13755-023-00255-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
The fractal features of liver fibrosis MR images exhibit an irregular fragmented distribution, and the diffuse feature distribution lacks interconnectivity, result- ing in incomplete feature learning and poor recognition accuracy. In this paper, we insert recursive gated convolution into the ResNet18 network to introduce spatial information interactions during the feature learning process and extend it to higher orders using recursion. Higher-order spatial information interactions enhance the correlation between features and enable the neural network to focus more on the pixel-level dependencies, enabling a global interpretation of liver MR images. Additionally, the existence of light scattering and quantum noise during the imaging process, coupled with environmental factors such as breathing artifacts caused by long time breath holding, affects the quality of the MR images. To improve the classification performance of the neural network and better cap- ture sample features, we introduce the Adaptive Rebalance loss function and incorporate the feature paradigm as a learnable adaptive attribute into the angular margin auxiliary function. Adaptive Rebalance loss function can expand the inter-class distance and narrow the intra-class difference to further enhance discriminative ability of the model. We conduct extensive experiments on liver fibrosis MR imaging involving 209 patients. The results demonstrate an average improvement of two percent in recognition accuracy compared to ResNet18. The github is at https://github.com/XZN1233/paper.git.
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Affiliation(s)
- Ling Zhang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Zhennan Xiao
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Wenchao Jiang
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Chengbin Luo
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Ming Ye
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006 Guangdong China
| | - Guanghui Yue
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060 Guangdong China
| | - Zhiyuan Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
| | - Shuman Ouyang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
| | - Yupin Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120 Guangdong China
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6
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Li M, Jiang Y, Zhang Y, Zhu H. Medical image analysis using deep learning algorithms. Front Public Health 2023; 11:1273253. [PMID: 38026291 PMCID: PMC10662291 DOI: 10.3389/fpubh.2023.1273253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/05/2023] [Indexed: 12/01/2023] Open
Abstract
In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.
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Affiliation(s)
- Mengfang Li
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuanyuan Jiang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanzhou Zhang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haisheng Zhu
- Department of Cardiovascular Medicine, Wencheng People’s Hospital, Wencheng, China
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7
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Mackenzie A, Lewis E, Loveland J. Successes and challenges in extracting information from DICOM image databases for audit and research. Br J Radiol 2023; 96:20230104. [PMID: 37698251 PMCID: PMC10607388 DOI: 10.1259/bjr.20230104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 09/13/2023] Open
Abstract
In radiography, much valuable associated data (metadata) is generated during image acquisition. The current setup of picture archive and communication systems (PACS) can make extraction of this metadata difficult, especially as it is typically stored with the image. The aim of this work is to examine the current challenges in extracting image metadata and to discuss the potential benefits of using this rich information. This work focuses on breast screening, though the conclusions are applicable to other modalities.The data stored in PACS contain information, currently underutilised, and is of great benefit for auditing and improving imaging and radiographic practice. From the literature, we present examples of the potential clinical benefit such as audits of dose, and radiographic practice, as well as more advanced research highlighting the effects of radiographic practice, e.g. cancer detection rates affected by imaging technology.This review considers the challenges in extracting data, namely,• The search tools for data on most PACS are inadequate being both time-consuming and limited in elements that can be searched.• Security and information governance considerations• Anonymisation of data if required• Data curationThe review describes some solutions that have been successfully implemented.• Retrospective extraction: direct query on PACS• Extracting data prospectively• Use of structured reports• Use of trusted research environmentsUltimately, the data access process will be made easier by inclusion during PACS procurement. Auditing data from PACS can be used to improve quality of imaging and workflow, all of which will be a clinical benefit to patients.
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Affiliation(s)
| | | | - John Loveland
- NCCPM, Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
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8
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Debelee TG. Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review. Diagnostics (Basel) 2023; 13:3147. [PMID: 37835889 PMCID: PMC10572538 DOI: 10.3390/diagnostics13193147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/22/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.
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Affiliation(s)
- Taye Girma Debelee
- Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia;
- Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa 16417, Ethiopia
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9
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Ashurov A, Chelloug SA, Tselykh A, Muthanna MSA, Muthanna A, Al-Gaashani MSAM. Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism. Life (Basel) 2023; 13:1945. [PMID: 37763348 PMCID: PMC10532552 DOI: 10.3390/life13091945] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/16/2023] [Accepted: 09/17/2023] [Indexed: 09/29/2023] Open
Abstract
Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection. Computer-assisted pathological image classification is of paramount importance for breast cancer diagnosis. This study introduces a novel approach to breast cancer histopathological image classification. It leverages modified pre-trained CNN models and attention mechanisms to enhance model interpretability and robustness, emphasizing localized features and enabling accurate discrimination of complex cases. Our method involves transfer learning with deep CNN models-Xception, VGG16, ResNet50, MobileNet, and DenseNet121-augmented with the convolutional block attention module (CBAM). The pre-trained models are finetuned, and the two CBAM models are incorporated at the end of the pre-trained models. The models are compared to state-of-the-art breast cancer diagnosis approaches and tested for accuracy, precision, recall, and F1 score. The confusion matrices are used to evaluate and visualize the results of the compared models. They help in assessing the models' performance. The test accuracy rates for the attention mechanism (AM) using the Xception model on the "BreakHis" breast cancer dataset are encouraging at 99.2% and 99.5%. The test accuracy for DenseNet121 with AMs is 99.6%. The proposed approaches also performed better than previous approaches examined in the related studies.
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Affiliation(s)
- Asadulla Ashurov
- School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
| | - Samia Allaoua Chelloug
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Alexey Tselykh
- Institute of Computer Technologies and Information Security, Southern Federal University, Taganrog 347922, Russia; (A.T.); (M.S.A.M.)
| | - Mohammed Saleh Ali Muthanna
- Institute of Computer Technologies and Information Security, Southern Federal University, Taganrog 347922, Russia; (A.T.); (M.S.A.M.)
| | - Ammar Muthanna
- RUDN University, 6 Miklukho-Maklaya Street, Moscow 117198, Russia;
| | - Mehdhar S. A. M. Al-Gaashani
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
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Karimzadeh M, Vakanski A, Xian M, Zhang B. POST-HOC EXPLAINABILITY OF BI-RADS DESCRIPTORS IN A MULTI-TASK FRAMEWORK FOR BREAST CANCER DETECTION AND SEGMENTATION. IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING : [PROCEEDINGS]. IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING 2023; 2023:10.1109/mlsp55844.2023.10286006. [PMID: 38572141 PMCID: PMC10989244 DOI: 10.1109/mlsp55844.2023.10286006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women. Although machine learning-based Computer-Aided Diagnosis (CAD) systems have shown potential to assist radiologists in analyzing medical images, the opaque nature of the best-performing CAD systems has raised concerns about their trustworthiness and interpretability. This paper proposes MT-BI-RADS, a novel explainable deep learning approach for tumor detection in Breast Ultrasound (BUS) images. The approach offers three levels of explanations to enable radiologists to comprehend the decision-making process in predicting tumor malignancy. Firstly, the proposed model outputs the BI-RADS categories used for BUS image analysis by radiologists. Secondly, the model employs multitask learning to concurrently segment regions in images that correspond to tumors. Thirdly, the proposed approach outputs quantified contributions of each BI-RADS descriptor toward predicting the benign or malignant class using post-hoc explanations with Shapley Values.
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Affiliation(s)
| | | | - Min Xian
- Department of Computer Science, University of Idaho, Idaho Falls, USA
| | - Boyu Zhang
- Department of Computer Science, University of Idaho, Idaho Falls, USA
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11
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Li Y, Liu S. Adversarial Attack and Defense in Breast Cancer Deep Learning Systems. Bioengineering (Basel) 2023; 10:973. [PMID: 37627858 PMCID: PMC10451783 DOI: 10.3390/bioengineering10080973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
Deep-learning-assisted medical diagnosis has brought revolutionary innovations to medicine. Breast cancer is a great threat to women's health, and deep-learning-assisted diagnosis of breast cancer pathology images can save manpower and improve diagnostic accuracy. However, researchers have found that deep learning systems based on natural images are vulnerable to attacks that can lead to errors in recognition and classification, raising security concerns about deep systems based on medical images. We used the adversarial attack algorithm FGSM to reveal that breast cancer deep learning systems are vulnerable to attacks and thus misclassify breast cancer pathology images. To address this problem, we built a deep learning system for breast cancer pathology image recognition with better defense performance. Accurate diagnosis of medical images is related to the health status of patients. Therefore, it is very important and meaningful to improve the security and reliability of medical deep learning systems before they are actually deployed.
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Affiliation(s)
- Yang Li
- Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8511, Japan
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12
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Yu S, Jin M, Wen T, Zhao L, Zou X, Liang X, Xie Y, Pan W, Piao C. Accurate breast cancer diagnosis using a stable feature ranking algorithm. BMC Med Inform Decis Mak 2023; 23:64. [PMID: 37024893 PMCID: PMC10080822 DOI: 10.1186/s12911-023-02142-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/14/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Breast cancer (BC) is one of the most common cancers among women. Since diverse features can be collected, how to stably select the powerful ones for accurate BC diagnosis remains challenging. METHODS A hybrid framework is designed for successively investigating both feature ranking (FR) stability and cancer diagnosis effectiveness. Specifically, on 4 BC datasets (BCDR-F03, WDBC, GSE10810 and GSE15852), the stability of 23 FR algorithms is evaluated via an advanced estimator (S), and the predictive power of the stable feature ranks is further tested by using different machine learning classifiers. RESULTS Experimental results identify 3 algorithms achieving good stability ([Formula: see text]) on the four datasets and generalized Fisher score (GFS) leading to state-of-the-art performance. Moreover, GFS ranks suggest that shape features are crucial in BC image analysis (BCDR-F03 and WDBC) and that using a few genes can well differentiate benign and malignant tumor cases (GSE10810 and GSE15852). CONCLUSIONS The proposed framework recognizes a stable FR algorithm for accurate BC diagnosis. Stable and effective features could deepen the understanding of BC diagnosis and related decision-making applications.
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Affiliation(s)
- Shaode Yu
- School of Information and Communication Engineering, Communication University of China, Beijing, China
| | - Mingxue Jin
- School of Information and Communication Engineering, Communication University of China, Beijing, China
| | - Tianhang Wen
- Department of Radiology, The Second Affiliated Hospital of Shenyang Medical College, Shenyang, China
| | - Linlin Zhao
- Department of Radiology, The Second Affiliated Hospital of Shenyang Medical College, Shenyang, China
| | - Xuechao Zou
- Department of Radiology, The Second Affiliated Hospital of Shenyang Medical College, Shenyang, China
| | - Xiaokun Liang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wanlong Pan
- Experimental Teaching Center for Pathogen Biology and Immunology, North Sichuan Medical College, Nanchong, China
| | - Chenghao Piao
- Department of Radiology, The Second Affiliated Hospital of Shenyang Medical College, Shenyang, China.
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13
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Zhu Z, Wang SH, Zhang YD. A Survey of Convolutional Neural Network in Breast Cancer. COMPUTER MODELING IN ENGINEERING & SCIENCES : CMES 2023; 136:2127-2172. [PMID: 37152661 PMCID: PMC7614504 DOI: 10.32604/cmes.2023.025484] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/28/2022] [Indexed: 05/09/2023]
Abstract
Problems For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD). Methods We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation. Conclusion Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.
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Affiliation(s)
| | | | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
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14
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Ayana G, Dese K, Dereje Y, Kebede Y, Barki H, Amdissa D, Husen N, Mulugeta F, Habtamu B, Choe SW. Vision-Transformer-Based Transfer Learning for Mammogram Classification. Diagnostics (Basel) 2023; 13:diagnostics13020178. [PMID: 36672988 PMCID: PMC9857963 DOI: 10.3390/diagnostics13020178] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/27/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer.
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Affiliation(s)
- Gelan Ayana
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
- School of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia
| | - Kokeb Dese
- School of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia
| | - Yisak Dereje
- Department of Information Engineering, Marche Polytechnic University, 60121 Ancona, Italy
| | - Yonas Kebede
- Biomedical Engineering Unit, Black Lion Hospital, Addis Ababa University, Addis Ababa 1000, Ethiopia
| | - Hika Barki
- Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Republic of Korea
| | - Dechassa Amdissa
- Department of Basic and Applied Science for Engineering, Sapienza University of Rome, 00161 Roma, Italy
| | - Nahimiya Husen
- Department of Bioengineering and Robotics, Campus Bio-Medico University of Rome, 00128 Roma, Italy
| | - Fikadu Mulugeta
- Center of Biomedical Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 1000, Ethiopia
| | - Bontu Habtamu
- School of Biomedical Engineering, Jimma University, Jimma 378, Ethiopia
| | - Se-Woon Choe
- Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
- Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39253, Republic of Korea
- Correspondence: ; Tel.: +82-54-478-7781; Fax: +82-54-462-1049
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15
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Zhao Y, Zhang J, Hu D, Qu H, Tian Y, Cui X. Application of Deep Learning in Histopathology Images of Breast Cancer: A Review. MICROMACHINES 2022; 13:2197. [PMID: 36557496 PMCID: PMC9781697 DOI: 10.3390/mi13122197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/04/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.
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Affiliation(s)
- Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110169, China
- Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110169, China
| | - Jie Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Dayu Hu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Hui Qu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Ye Tian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110169, China
- Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang 110169, China
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16
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Khan N, Adam R, Huang P, Maldjian T, Duong TQ. Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review. Tomography 2022; 8:2784-2795. [PMID: 36412691 PMCID: PMC9680498 DOI: 10.3390/tomography8060232] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/12/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer.
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17
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Yang K, Suzuki A, Ye J, Nosato H, Izumori A, Sakanashi H. CTG-Net: Cross-task guided network for breast ultrasound diagnosis. PLoS One 2022; 17:e0271106. [PMID: 35951606 PMCID: PMC9371312 DOI: 10.1371/journal.pone.0271106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/24/2022] [Indexed: 11/19/2022] Open
Abstract
Deep learning techniques have achieved remarkable success in lesion segmentation and classification between benign and malignant tumors in breast ultrasound images. However, existing studies are predominantly focused on devising efficient neural network-based learning structures to tackle specific tasks individually. By contrast, in clinical practice, sonographers perform segmentation and classification as a whole; they investigate the border contours of the tissue while detecting abnormal masses and performing diagnostic analysis. Performing multiple cognitive tasks simultaneously in this manner facilitates exploitation of the commonalities and differences between tasks. Inspired by this unified recognition process, this study proposes a novel learning scheme, called the cross-task guided network (CTG-Net), for efficient ultrasound breast image understanding. CTG-Net integrates the two most significant tasks in computerized breast lesion pattern investigation: lesion segmentation and tumor classification. Further, it enables the learning of efficient feature representations across tasks from ultrasound images and the task-specific discriminative features that can greatly facilitate lesion detection. This is achieved using task-specific attention models to share the prediction results between tasks. Then, following the guidance of task-specific attention soft masks, the joint feature responses are efficiently calibrated through iterative model training. Finally, a simple feature fusion scheme is used to aggregate the attention-guided features for efficient ultrasound pattern analysis. We performed extensive experimental comparisons on multiple ultrasound datasets. Compared to state-of-the-art multi-task learning approaches, the proposed approach can improve the Dice's coefficient, true-positive rate of segmentation, AUC, and sensitivity of classification by 11%, 17%, 2%, and 6%, respectively. The results demonstrate that the proposed cross-task guided feature learning framework can effectively fuse the complementary information of ultrasound image segmentation and classification tasks to achieve accurate tumor localization. Thus, it can aid sonographers to detect and diagnose breast cancer.
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Affiliation(s)
- Kaiwen Yang
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Aiga Suzuki
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Jiaxing Ye
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Hirokazu Nosato
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | | | - Hidenori Sakanashi
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
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18
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Ma CY, Zhou JY, Xu XT, Qin SB, Han MF, Cao XH, Gao YZ, Xu L, Zhou JJ, Zhang W, Jia LC. Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer. BMC Med Imaging 2022; 22:123. [PMID: 35810273 PMCID: PMC9271246 DOI: 10.1186/s12880-022-00851-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives Accurate contouring of the clinical target volume (CTV) is a key element of radiotherapy in cervical cancer. We validated a novel deep learning (DL)-based auto-segmentation algorithm for CTVs in cervical cancer called the three-channel adaptive auto-segmentation network (TCAS). Methods A total of 107 cases were collected and contoured by senior radiation oncologists (ROs). Each case consisted of the following: (1) contrast-enhanced CT scan for positioning, (2) the related CTV, (3) multiple plain CT scans during treatment and (4) the related CTV. After registration between (1) and (3) for the same patient, the aligned image and CTV were generated. Method 1 is rigid registration, method 2 is deformable registration, and the aligned CTV is seen as the result. Method 3 is rigid registration and TCAS, method 4 is deformable registration and TCAS, and the result is generated by a DL-based method. Results From the 107 cases, 15 pairs were selected as the test set. The dice similarity coefficient (DSC) of method 1 was 0.8155 ± 0.0368; the DSC of method 2 was 0.8277 ± 0.0315; the DSCs of method 3 and 4 were 0.8914 ± 0.0294 and 0.8921 ± 0.0231, respectively. The mean surface distance and Hausdorff distance of methods 3 and 4 were markedly better than those of method 1 and 2. Conclusions The TCAS achieved comparable accuracy to the manual delineation performed by senior ROs and was significantly better than direct registration.
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Affiliation(s)
- Chen-Ying Ma
- Department of Radiation Oncology, 1st Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215123, China
| | - Ju-Ying Zhou
- Department of Radiation Oncology, 1st Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215123, China.
| | - Xiao-Ting Xu
- Department of Radiation Oncology, 1st Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215123, China
| | - Song-Bing Qin
- Department of Radiation Oncology, 1st Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou, 215123, China
| | - Miao-Fei Han
- Shanghai United Imaging Healthcare, Co. Ltd., Jiading, 201807, China
| | - Xiao-Huan Cao
- Shanghai United Imaging Healthcare, Co. Ltd., Jiading, 201807, China
| | - Yao-Zong Gao
- Shanghai United Imaging Healthcare, Co. Ltd., Jiading, 201807, China
| | - Lu Xu
- Shanghai United Imaging Healthcare, Co. Ltd., Jiading, 201807, China
| | - Jing-Jie Zhou
- Shanghai United Imaging Healthcare, Co. Ltd., Jiading, 201807, China
| | - Wei Zhang
- Shanghai United Imaging Healthcare, Co. Ltd., Jiading, 201807, China
| | - Le-Cheng Jia
- United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, 518045, China
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19
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Deep learning-based quality-controlled spleen assessment from ultrasound images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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20
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MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07441-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractMitosis assessment of breast cancer has a strong prognostic importance and is visually evaluated by pathologists. The inter, and intra-observer variability of this assessment is high. In this paper, a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer. Moreover, this paper introduces two new datasets. The first dataset is used to detect the nucleus in the WSIs, which contains 139,124 annotated nuclei in 1749 patches extracted from 115 WSIs of breast cancer tissue, and the second dataset consists of 4908 mitotic cells and 4908 non-mitotic cells image samples extracted from 214 WSIs which is used for mitosis classification. The created datasets are used to train the MITNET network, which consists of two deep learning architectures, called MITNET-det and MITNET-rec, respectively, to isolate nuclei cells and identify the mitoses in WSIs. In MITNET-det architecture, to extract features from nucleus images and fuse them, CSPDarknet and Path Aggregation Network (PANet) are used, respectively, and then, a detection strategy using You Look Only Once (scaled-YOLOv4) is employed to detect nucleus at three different scales. In the classification part, the detected isolated nucleus images are passed through proposed MITNET-rec deep learning architecture, to identify the mitosis in the WSIs. Various deep learning classifiers and the proposed classifier are trained with a publicly available mitosis datasets (MIDOG and ATYPIA) and then, validated over our created dataset. The results verify that deep learning-based classifiers trained on MIDOG and ATYPIA have difficulties to recognize mitosis on our dataset which shows that the created mitosis dataset has unique features and characteristics. Besides this, the proposed classifier outperforms the state-of-the-art classifiers significantly and achieves a $$68.7\%$$
68.7
%
F1-score and $$49.0\%$$
49.0
%
F1-score on the MIDOG and the created mitosis datasets, respectively. Moreover, the experimental results reveal that the overall proposed MITNET framework detects the nucleus in WSIs with high detection rates and recognizes the mitotic cells in WSI with high F1-score which leads to the improvement of the accuracy of pathologists’ decision.
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21
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Wang YY, Hamad AS, Palaniappan K, Lever TE, Bunyak F. LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos. Comput Biol Med 2022; 144:105339. [PMID: 35263687 PMCID: PMC8995389 DOI: 10.1016/j.compbiomed.2022.105339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/18/2022] [Accepted: 02/19/2022] [Indexed: 11/28/2022]
Abstract
The vocal folds (VFs) are a pair of muscles in the larynx that play a critical role in breathing, swallowing, and speaking. VF function can be adversely affected by various medical conditions including head or neck injuries, stroke, tumor, and neurological disorders. In this paper, we propose a deep learning system for automated detection of laryngeal adductor reflex (LAR) events in laryngeal endoscopy videos to enable objective, quantitative analysis of VF function. The proposed deep learning system incorporates our novel orthogonal region selection network and temporal context. This network learns to directly map its input to a VF open/close state without first segmenting or tracking the VF region. This one-step approach drastically reduces manual annotation needs from labor-intensive segmentation masks or VF motion tracks to frame-level class labels. The proposed spatio-temporal network with an orthogonal region selection subnetwork allows integration of local image features, global image features, and VF state information in time for robust LAR event detection. The proposed network is evaluated against several network variations that incorporate temporal context and is shown to lead to better performance. The experimental results show promising performance for automated, objective, and quantitative analysis of LAR events from laryngeal endoscopy videos with over 90% and 99% F1 scores for LAR and non-LAR frames respectively.
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Affiliation(s)
- Yang Yang Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA
| | - Ali S Hamad
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA
| | - Kannappan Palaniappan
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA
| | - Teresa E Lever
- Department of Otolaryngology - Head and Neck Surgery, University of Missouri, Columbia, 65211, Missouri, USA
| | - Filiz Bunyak
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA.
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22
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Azevedo V, Silva C, Dutra I. Quantum transfer learning for breast cancer detection. QUANTUM MACHINE INTELLIGENCE 2022; 4:5. [PMID: 35252762 PMCID: PMC8883238 DOI: 10.1007/s42484-022-00062-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Abstract
One of the areas with the potential to be explored in quantum computing (QC) is machine learning (ML), giving rise to quantum machine learning (QML). In an era when there is so much data, ML may benefit from either speed, complexity or smaller amounts of storage. In this work, we explore a quantum approach to a machine learning problem. Based on the work of Mari et al., we train a set of hybrid classical-quantum neural networks using transfer learning (TL). Our task was to solve the problem of classifying full-image mammograms into malignant and benign, provided by BCDR. Throughout the course of our work, heatmaps were used to highlight the parts of the mammograms that were being targeted by the networks while evaluating different performance metrics. Our work shows that this method may hold benefits regarding the generalization of complex data; however, further tests are needed. We also show that, depending on the task, some architectures perform better than others. Nonetheless, our results were superior to those reported in the state-of-the-art (accuracy of 84% against 76.9%, respectively). In addition, experiments were conducted in a real quantum device, and results were compared with the classical and simulator.
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Affiliation(s)
- Vanda Azevedo
- Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Carla Silva
- Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Inês Dutra
- CINTESIS and Department of Computer Science, Faculty of Sciences, University of Porto, Porto, Portugal
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23
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Abstract
Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques. In this work, we have identified the most common and damaging diseases affecting cereal crop production, and we also reviewed 45 works performed on the detection and classification of various diseases that occur on six cereal crops within the past five years. In addition, we identified and summarised numerous publicly available datasets for each cereal crop, which the lack thereof we identified as the main challenges faced for researching the application of machine learning in cereal crop detection. In this survey, we identified deep convolutional neural networks trained on hyperspectral data as the most effective approach for early detection of diseases and transfer learning as the most commonly used and yielding the best result training method.
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24
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Alzaid A, Wignall A, Dogramadzi S, Pandit H, Xie SQ. Automatic detection and classification of peri-prosthetic femur fracture. Int J Comput Assist Radiol Surg 2022; 17:649-660. [PMID: 35157227 PMCID: PMC8948116 DOI: 10.1007/s11548-021-02552-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 12/21/2021] [Indexed: 12/02/2022]
Abstract
Purpose Object classification and localization is a key task of computer-aided diagnosis (CAD) tool. Although there have been numerous generic deep learning (DL) models developed for CAD, there is no work in the literature to evaluate their effectiveness when utilized in diagnosing fractures in proximity of joint implants. In this work, we aim to assess the performance of existing classification systems on binary and multi-class problems (fracture types) using plain radiographs. In addition, we evaluated the performance of object detection systems using the one- and two-stage DL architectures. Methods A data set of 1272 X-ray images of Peri-prosthetic Femur Fracture PFF was collected. The fractures were annotated with bounding boxes and classified according to the Vancouver Classification System (type A, B, C) by two clinical specialists. Four classification models such as Densenet161, Resnet50, Inception, VGG and two object detection models such as Faster RCNN and RetinaNet were evaluated, and their performance compared. Six confusion matrix-based measures were reported to evaluate fracture classification. For localization of the fracture, Average Precision and localization accuracy were reported. Results The Resnet50 showed the best performance with \documentclass[12pt]{minimal}
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\begin{document}$$94\%$$\end{document}94% F1-score in the binary classification: fracture/normal. In addition, the Resnet50 showed \documentclass[12pt]{minimal}
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\begin{document}$$90\%$$\end{document}90% accuracy in multi-classification (normal, Vancouver type A, B and C). Conclusions A large data set of PFF images and the annotations of fracture features by two independent assessments were created to implement a DL-based approach for detecting, classifying and localizing PFFs. It was shown that this approach could be a promising diagnostic tool of fractures in proximity of joint implants.
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Affiliation(s)
- Asma Alzaid
- School of Electrical and Electronic Engineering, University of Leeds, Leeds, LS2 9JT, UK.
| | | | - Sanja Dogramadzi
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
| | - Hemant Pandit
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Leeds Institute of Rheumatic and Musculoskeletal Medicine, Leeds, UK
| | - Sheng Quan Xie
- School of Electrical and Electronic Engineering, University of Leeds, Leeds, LS2 9JT, UK. .,Collaborates with Institute of Rehabilitation Engineering, Binzhou Medical University, Yantai, China.
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25
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Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06804-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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26
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Al Noumah W, Jafar A, Al Joumaa K. Using parallel pre-trained types of DCNN model to predict breast cancer with color normalization. BMC Res Notes 2022; 15:14. [PMID: 35012681 PMCID: PMC8751220 DOI: 10.1186/s13104-021-05902-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/23/2021] [Indexed: 11/23/2022] Open
Abstract
Objective Breast cancer is the most common among women, and it causes many deaths every year. Early diagnosis increases the chance of cure through treatment. The traditional manual diagnosis requires effort and time from pathological experts, as it needs a joint experience of a number of pathologists. Diagnostic mistakes can lead to catastrophic results and endanger the lives of patients. The presence of an expert system that is able to specify whether the examined tissue is healthy or not, thus improves the quality of diagnosis and saves the time of experts. In this paper, a model capable of classifying breast cancer anatomy by making use of a pre-trained DCNN has been proposed. To build this model, first of all the image should be color stained by using Vahadane algorithm, then the model which combines three pre-trained DCNN (Xception, NASNet and Inceptoin_Resnet_V2) should be built in parallel, then the three branches should be aggregated to take advantage of each other. The suggested model was tested under different values of threshold ratios and also compared with other models. Results The proposed model on the BreaKHis dataset achieved 98% accuracy, which is better than the accuracy of other models used in this field. Supplementary Information The online version contains supplementary material available at 10.1186/s13104-021-05902-3.
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Affiliation(s)
- William Al Noumah
- Department of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, Syria.
| | - Assef Jafar
- Department of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, Syria
| | - Kadan Al Joumaa
- Department of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, Syria
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A Hybrid Machine Learning Model Based on Global and Local Learner Algorithms for Diabetes Mellitus Prediction. JOURNAL OF BIOMIMETICS BIOMATERIALS AND BIOMEDICAL ENGINEERING 2022. [DOI: 10.4028/www.scientific.net/jbbbe.54.65] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Health is a critical condition for living things, even before the technology exists. Nowadays the healthcare domain provides a lot of scope for research as it has extremely evolved. The most researched areas of health sectors include diabetes mellitus (DM), breast cancer, brain tumor, etc. DM is a severe chronic disease that affects human health and has a high rate throughout the world. Early prediction of DM is important to reduce its risk and even avoid it. In this study, we propose a DM prediction model based on global and local learner algorithms. The proposed global and local learners stacking (GLLS) model; combines the prediction algorithms from two largely different but complementary machine learning paradigms, specifically XGBoost and NB from global learning whereas kNN and SVM (with RBF kernel) from local learning and aggregates them by stacking ensemble technique using LR as meta-learner. The effectiveness of the GLLS model was proved by comparing several performance measures and the results of different contrast experiments. The evaluation results on UCI Pima Indian diabetes data-set (PIDD) indicates the model has achieved the better prediction performance of 99.5%, 99.5%, 99.5%, 99.1%, and 100% in terms of accuracy, AUC, F1 score, sensitivity, and specificity respectively, compared to other research results mentioned in the literature. Moreover, to better validate the GLLS model performance, three additional medical data sets; Messidor, WBC, ILPD, are considered and the model also achieved an accuracy of 82.1%, 98.6%, and 89.3% respectively. Experimental results proved the effectiveness and superiority of our proposed GLLS model.
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Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review. J Med Syst 2021; 46:7. [PMID: 34860316 PMCID: PMC8642363 DOI: 10.1007/s10916-021-01786-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022]
Abstract
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
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Affiliation(s)
- R Rashmi
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
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Elameer AS, Jaber MM, Abd SK. Radiography image analysis using cat swarm optimized deep belief networks. JOURNAL OF INTELLIGENT SYSTEMS 2021; 31:40-54. [DOI: 10.1515/jisys-2021-0172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
Abstract
Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies.
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Affiliation(s)
- Amer S. Elameer
- Biomedical Informatics College, University of Information Technology and Communications (UOITC) , Baghdad , Iraq
| | - Mustafa Musa Jaber
- Department of Computer Science, Dijlah University Collage , Baghdad , 00964 , Iraq
- Department of Computer Science, Al-Turath University College , Baghdad , Iraq
| | - Sura Khalil Abd
- Department of Computer Science, Dijlah University Collage , Baghdad , 00964 , Iraq
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Waldamichael FG, Debelee TG, Ayano YM. Coffee disease detection using a robust HSV color‐based segmentation and transfer learning for use on smartphones. INT J INTELL SYST 2021. [DOI: 10.1002/int.22747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
| | - Taye Girma Debelee
- Research and Development Cluster Ethiopian Artificial Intelligence Center Addis Ababa Ethiopia
- Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia
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Karthik R, Menaka R, Kathiresan G, Anirudh M, Nagharjun M. Gaussian Dropout Based Stacked Ensemble CNN for Classification of Breast Tumor in Ultrasound Images. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.10.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Rufo DD, Debelee TG, Ibenthal A, Negera WG. Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM). Diagnostics (Basel) 2021; 11:1714. [PMID: 34574055 PMCID: PMC8467876 DOI: 10.3390/diagnostics11091714] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/06/2021] [Accepted: 09/17/2021] [Indexed: 12/01/2022] Open
Abstract
Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing, which presents new research challenges and opportunities. In this paper, we propose a preemptive diagnosis method for diabetes mellitus (DM) to assist or complement the early recognition of the disease in countries with low medical expert densities. Diabetes data are collected from the Zewditu Memorial Hospital (ZMHDD) in Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) is one of the most recent successful research findings for the gradient boosting framework that uses tree-based learning algorithms. It has low computational complexity and, therefore, is suited for applications in limited capacity regions such as Ethiopia. Thus, in this study, we apply the principle of LightGBM to develop an accurate model for the diagnosis of diabetes. The experimental results show that the prepared diabetes dataset is informative to predict the condition of diabetes mellitus. With accuracy, AUC, sensitivity, and specificity of 98.1%, 98.1%, 99.9%, and 96.3%, respectively, the LightGBM model outperformed KNN, SVM, NB, Bagging, RF, and XGBoost in the case of the ZMHDD dataset.
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Affiliation(s)
- Derara Duba Rufo
- College of Engineering and Technology, Dilla University, Dilla 419, Ethiopia;
| | - Taye Girma Debelee
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia;
- Ethiopian Artificial Intelligence Center, Addis Ababa 40782, Ethiopia;
| | - Achim Ibenthal
- Faculty of Engineering and Health, HAWK Universityof Applied Sciences and Arts, 37085 Göttingen, Germany
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Biratu ES, Schwenker F, Ayano YM, Debelee TG. A Survey of Brain Tumor Segmentation and Classification Algorithms. J Imaging 2021; 7:jimaging7090179. [PMID: 34564105 PMCID: PMC8465364 DOI: 10.3390/jimaging7090179] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/25/2021] [Accepted: 08/28/2021] [Indexed: 01/16/2023] Open
Abstract
A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models' performance evaluation metrics.
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Affiliation(s)
- Erena Siyoum Biratu
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia; (E.S.B.); (T.G.D.)
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, Ulm University, 89081 Ulm, Germany
- Correspondence:
| | | | - Taye Girma Debelee
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia; (E.S.B.); (T.G.D.)
- Ethiopian Artificial Intelligence Center, Addis Ababa 40782, Ethiopia;
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Yu X, Zhou Q, Wang S, Zhang Y. A systematic survey of deep learning in breast cancer. INT J INTELL SYST 2021. [DOI: 10.1002/int.22622] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Xiang Yu
- School of Computing and Mathematical Sciences University of Leicester Leicester, Leicestershire UK
| | - Qinghua Zhou
- School of Computing and Mathematical Sciences University of Leicester Leicester, Leicestershire UK
| | - Shuihua Wang
- School of Computing and Mathematical Sciences University of Leicester Leicester, Leicestershire UK
| | - Yu‐Dong Zhang
- School of Computing and Mathematical Sciences University of Leicester Leicester, Leicestershire UK
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Huo L, Hu X, Xiao Q, Gu Y, Chu X, Jiang L. Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images. Magn Reson Imaging 2021; 82:31-41. [PMID: 34147598 DOI: 10.1016/j.mri.2021.06.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/14/2021] [Accepted: 06/15/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Segmentation of the whole breast and fibroglandular tissue (FGT) is important for quantitatively analyzing the breast cancer risk in the dynamic contrast-enhanced magnetic resonance (DCE-MR) images. The purpose of this study is to improve the accuracy and efficiency of the segmentation of the whole breast and FGT in 3-D fat-suppressed DCE-MR images with a versatile deep learning (DL) framework. METHODS We randomly collected 100 breast DCE-MR scans from Shanghai Cancer Hospital of Fudan University. The MR scans in the dataset were different in both the spatial resolution and the MR scanners employed. Furthermore, four breast density categories were assessed by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. The dataset was separated into the training and the testing sets, while keeping a balanced distribution of scans with different imaging parameters and density categories. The nnU-Net has been recently proposed to automatically adapt preprocessing strategies and network architectures for a given medical image dataset, thus showing a great potential in the systematic adaptation of DL methods to different datasets. In this study, we applied the nnU-Net to segment the whole breast and FGT in 3-D fat-suppressed DCE-MR images. Five-fold cross validation was employed to train and validate the segmentation method. RESULTS The segmentation performance was evaluated with the volume and surface agreement metrics between the DL-based automatic and the manually delineated masks, as quantified with the following measures: the average Dice volume overlap (0.968 ± 0.017 and 0.877 ± 0.081), the average surface distances (0.201 ± 0.080 mm and 0.310 ± 0.043 mm), and the Pearson correlation coefficient of masks (0.995 and 0.972) between the automatic and the manually delineated masks, as calculated for the whole breast and the FGT segmentation, respectively. The correlation coefficient between the breast densities obtained with the DL-based segmentation and the manual delineation was 0.981. There was a positive bias of 0.8% (DL-based relative to manual) in breast density measurement with the Bland-Altman plot. The execution time of the DL-based segmentation was approximately 20 s for the whole breast segmentation and 15 s for the FGT segmentation. CONCLUSIONS Our DL-based segmentation framework using nnU-Net could robustly achieve high accuracy and efficiency across variable MR imaging settings without extra pre- or post-processing procedures. It would be useful for developing DCE-MR-based CAD systems to quantify breast cancer risk and to be integrated into the clinical workflow.
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Affiliation(s)
- Lu Huo
- Center for Advanced Medical Imaging Technology, Shanghai Advanced Research Institute, Chinese Academy of Sciences, No.99 Haike Road, Shanghai 201200, China; University of Chinese Academy of Sciences, No.19 Yuquan Road, Beijing 100049, China; Shanghai United Imaging Healthcare Co., Ltd., No. 2258 Chengbei Road, Shanghai 201807, China
| | - Xiaoxin Hu
- Department of Radiology, Shanghai Cancer Hospital of Fudan University, No. 270 DongAn Road, Shanghai 200032, China
| | - Qin Xiao
- Department of Radiology, Shanghai Cancer Hospital of Fudan University, No. 270 DongAn Road, Shanghai 200032, China
| | - Yajia Gu
- Department of Radiology, Shanghai Cancer Hospital of Fudan University, No. 270 DongAn Road, Shanghai 200032, China
| | - Xu Chu
- Center for Advanced Medical Imaging Technology, Shanghai Advanced Research Institute, Chinese Academy of Sciences, No.99 Haike Road, Shanghai 201200, China; Shanghai United Imaging Healthcare Co., Ltd., No. 2258 Chengbei Road, Shanghai 201807, China
| | - Luan Jiang
- Center for Advanced Medical Imaging Technology, Shanghai Advanced Research Institute, Chinese Academy of Sciences, No.99 Haike Road, Shanghai 201200, China; Shanghai United Imaging Healthcare Co., Ltd., No. 2258 Chengbei Road, Shanghai 201807, China.
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Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features. SENSORS 2021; 21:s21113628. [PMID: 34071029 PMCID: PMC8197148 DOI: 10.3390/s21113628] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/10/2021] [Accepted: 05/14/2021] [Indexed: 11/23/2022]
Abstract
Breast cancer, like most forms of cancer, is a fatal disease that claims more than half a million lives every year. In 2020, breast cancer overtook lung cancer as the most commonly diagnosed form of cancer. Though extremely deadly, the survival rate and longevity increase substantially with early detection and diagnosis. The treatment protocol also varies with the stage of breast cancer. Diagnosis is typically done using histopathological slides from which it is possible to determine whether the tissue is in the Ductal Carcinoma In Situ (DCIS) stage, in which the cancerous cells have not spread into the encompassing breast tissue, or in the Invasive Ductal Carcinoma (IDC) stage, wherein the cells have penetrated into the neighboring tissues. IDC detection is extremely time-consuming and challenging for physicians. Hence, this can be modeled as an image classification task where pattern recognition and machine learning can be used to aid doctors and medical practitioners in making such crucial decisions. In the present paper, we use an IDC Breast Cancer dataset that contains 277,524 images (with 78,786 IDC positive images and 198,738 IDC negative images) to classify the images into IDC(+) and IDC(-). To that end, we use feature extractors, including textural features, such as SIFT, SURF and ORB, and statistical features, such as Haralick texture features. These features are then combined to yield a dataset of 782 features. These features are ensembled by stacking using various Machine Learning classifiers, such as Random Forest, Extra Trees, XGBoost, AdaBoost, CatBoost and Multi Layer Perceptron followed by feature selection using Pearson Correlation Coefficient to yield a dataset with four features that are then used for classification. From our experimental results, we found that CatBoost yielded the highest accuracy (92.55%), which is at par with other state-of-the-art results—most of which employ Deep Learning architectures. The source code is available in the GitHub repository.
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Choudhary T, Mishra V, Goswami A, Sarangapani J. A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices. Comput Biol Med 2021; 134:104432. [PMID: 33964737 DOI: 10.1016/j.compbiomed.2021.104432] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 04/16/2021] [Accepted: 04/21/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND AND OBJECTIVE A significant progress has been made in automated medical diagnosis with the advent of deep learning methods in recent years. However, deploying a deep learning model for mobile and small-scale, low-cost devices is a major bottleneck. Further, breast cancer is more prevalent currently, and ductal carcinoma being its most common type. Although many machine/deep learning methods have already been investigated, still, there is a need for further improvement. METHOD This paper proposes a novel deep convolutional neural network (CNN) based transfer learning approach complemented with structured filter pruning for histopathological image classification, and to bring down the run-time resource requirement of the trained deep learning models. In the proposed method, first, the less important filters are pruned from the convolutional layers and then the pruned models are trained on the histopathological image dataset. RESULTS We performed extensive experiments using three popular pre-trained CNNs, VGG19, ResNet34, and ResNet50. With VGG19 pruned model, we achieved an accuracy of 91.25% outperforming earlier methods on the same dataset and architecture while reducing 63.46% FLOPs. Whereas, with the ResNet34 pruned model, the accuracy increases to 91.80% with 40.63% fewer FLOPs. Moreover, with the ResNet50 model, we achieved an accuracy of 92.07% with 30.97% less FLOPs. CONCLUSION The experimental results reveal that the pre-trained model's performance complemented with filter pruning exceeds original pre-trained models. Another important outcome of the research is that the pruned model with reduced resource requirements can be deployed in point-of-care devices for automated diagnosis applications with ease.
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Affiliation(s)
| | - Vipul Mishra
- Bennett University, Greater Noida, Uttar Pradesh, 201310, India.
| | - Anurag Goswami
- Bennett University, Greater Noida, Uttar Pradesh, 201310, India.
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Abdelrahman L, Al Ghamdi M, Collado-Mesa F, Abdel-Mottaleb M. Convolutional neural networks for breast cancer detection in mammography: A survey. Comput Biol Med 2021; 131:104248. [PMID: 33631497 DOI: 10.1016/j.compbiomed.2021.104248] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/08/2021] [Accepted: 01/25/2021] [Indexed: 12/17/2022]
Abstract
Despite its proven record as a breast cancer screening tool, mammography remains labor-intensive and has recognized limitations, including low sensitivity in women with dense breast tissue. In the last ten years, Neural Network advances have been applied to mammography to help radiologists increase their efficiency and accuracy. This survey aims to present, in an organized and structured manner, the current knowledge base of convolutional neural networks (CNNs) in mammography. The survey first discusses traditional Computer Assisted Detection (CAD) and more recently developed CNN-based models for computer vision in mammography. It then presents and discusses the literature on available mammography training datasets. The survey then presents and discusses current literature on CNNs for four distinct mammography tasks: (1) breast density classification, (2) breast asymmetry detection and classification, (3) calcification detection and classification, and (4) mass detection and classification, including presenting and comparing the reported quantitative results for each task and the pros and cons of the different CNN-based approaches. Then, it offers real-world applications of CNN CAD algorithms by discussing current Food and Drug Administration (FDA) approved models. Finally, this survey highlights the potential opportunities for future work in this field. The material presented and discussed in this survey could serve as a road map for developing CNN-based solutions to improve mammographic detection of breast cancer further.
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Affiliation(s)
- Leila Abdelrahman
- University of Miami, Department of Electrical and Computer Engineering, Memorial Dr, Coral Gables, FL, 33146, USA
| | - Manal Al Ghamdi
- Umm Al-Qura University, Department of Computer Science, Alawali, Mecca, 24381, Saudi Arabia
| | - Fernando Collado-Mesa
- University of Miami Miller School of Medicine, Department of Radiology, 1115 NW 14th Street Miami, FL, 33136, USA
| | - Mohamed Abdel-Mottaleb
- University of Miami, Department of Electrical and Computer Engineering, Memorial Dr, Coral Gables, FL, 33146, USA.
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Biratu ES, Schwenker F, Debelee TG, Kebede SR, Negera WG, Molla HT. Enhanced Region Growing for Brain Tumor MR Image Segmentation. J Imaging 2021; 7:22. [PMID: 34460621 PMCID: PMC8321280 DOI: 10.3390/jimaging7020022] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 11/18/2022] Open
Abstract
A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and grows and multiplies abnormally. The unusual growth of cells within the brain or inside the skull, which can be cancerous or non-cancerous has been the reason for the death of adults in developed countries and children in under developing countries like Ethiopia. The studies have shown that the region growing algorithm initializes the seed point either manually or semi-manually which as a result affects the segmentation result. However, in this paper, we proposed an enhanced region-growing algorithm for the automatic seed point initialization. The proposed approach's performance was compared with the state-of-the-art deep learning algorithms using the common dataset, BRATS2015. In the proposed approach, we applied a thresholding technique to strip the skull from each input brain image. After the skull is stripped the brain image is divided into 8 blocks. Then, for each block, we computed the mean intensities and from which the five blocks with maximum mean intensities were selected out of the eight blocks. Next, the five maximum mean intensities were used as a seed point for the region growing algorithm separately and obtained five different regions of interest (ROIs) for each skull stripped input brain image. The five ROIs generated using the proposed approach were evaluated using dice similarity score (DSS), intersection over union (IoU), and accuracy (Acc) against the ground truth (GT), and the best region of interest is selected as a final ROI. Finally, the final ROI was compared with different state-of-the-art deep learning algorithms and region-based segmentation algorithms in terms of DSS. Our proposed approach was validated in three different experimental setups. In the first experimental setup where 15 randomly selected brain images were used for testing and achieved a DSS value of 0.89. In the second and third experimental setups, the proposed approach scored a DSS value of 0.90 and 0.80 for 12 randomly selected and 800 brain images respectively. The average DSS value for the three experimental setups was 0.86.
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Affiliation(s)
- Erena Siyoum Biratu
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia;
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, Ulm University, 89081 Ulm, Germany;
| | - Taye Girma Debelee
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia;
- Artificial Intelligence Center, Addis Ababa 40782, Ethiopia; (S.R.K.); (W.G.N.)
| | - Samuel Rahimeto Kebede
- Artificial Intelligence Center, Addis Ababa 40782, Ethiopia; (S.R.K.); (W.G.N.)
- Department of Electrical and Computer Engineering, Debreberhan University, Debre Berhan 445, Ethiopia
| | | | - Hasset Tamirat Molla
- College of Natural and Computational Science, Addis Ababa University, Addis Ababa 1176, Ethiopia;
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Chugh G, Kumar S, Singh N. Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis. Cognit Comput 2021. [DOI: 10.1007/s12559-020-09813-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Livieris IE, Stavroyiannis S, Pintelas E, Kotsilieris T, Pintelas P. A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index. EVOLVING SYSTEMS 2021. [DOI: 10.1007/s12530-020-09361-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Halling-Brown MD, Warren LM, Ward D, Lewis E, Mackenzie A, Wallis MG, Wilkinson LS, Given-Wilson RM, McAvinchey R, Young KC. OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data. Radiol Artif Intell 2021; 3:e200103. [PMID: 33937853 PMCID: PMC8082293 DOI: 10.1148/ryai.2020200103] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/03/2020] [Accepted: 10/05/2020] [Indexed: 11/11/2022]
Abstract
Supplemental material is available for this article.
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Affiliation(s)
- Mark D. Halling-Brown
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Lucy M. Warren
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Dominic Ward
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Emma Lewis
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Alistair Mackenzie
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Matthew G. Wallis
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Louise S. Wilkinson
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Rosalind M. Given-Wilson
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Rita McAvinchey
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Kenneth C. Young
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
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Pathak P, Jalal AS, Rai R. Breast Cancer Image Classification: A Review. Curr Med Imaging 2020; 17:720-740. [PMID: 33371857 DOI: 10.2174/0929867328666201228125208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/23/2020] [Accepted: 10/14/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer, such as mammogram, ultrasound, computed tomography and Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. OBJECTIVE This paper aims to cover the approaches used in the CAD system for the detection of breast cancer. METHODS In this paper, the methods used in CAD systems are categories into two classes: the conventional approach and artificial intelligence (AI) approach. RESULTS The conventional approach covers the basic steps of image processing, such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. CONCLUSION This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.
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Affiliation(s)
- Pooja Pathak
- Department of Mathematics, GLA University, Mathura, India
| | - Anand Singh Jalal
- Department of Computer Engineering & Applications, GLA University, Mathura, India
| | - Ritu Rai
- Department of Computer Engineering & Applications, GLA University, Mathura, India
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Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10228298] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.
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Debelee TG, Kebede SR, Schwenker F, Shewarega ZM. Deep Learning in Selected Cancers' Image Analysis-A Survey. J Imaging 2020; 6:121. [PMID: 34460565 PMCID: PMC8321208 DOI: 10.3390/jimaging6110121] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 10/19/2020] [Accepted: 10/26/2020] [Indexed: 02/08/2023] Open
Abstract
Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.
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Affiliation(s)
- Taye Girma Debelee
- Artificial Intelligence Center, 40782 Addis Ababa, Ethiopia; (S.R.K.); (Z.M.S.)
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, 120611 Addis Ababa, Ethiopia
| | - Samuel Rahimeto Kebede
- Artificial Intelligence Center, 40782 Addis Ababa, Ethiopia; (S.R.K.); (Z.M.S.)
- Department of Electrical and Computer Engineering, Debreberhan University, 445 Debre Berhan, Ethiopia
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, University of Ulm, 89081 Ulm, Germany;
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Gómez-Flores W, Coelho de Albuquerque Pereira W. A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound. Comput Biol Med 2020; 126:104036. [PMID: 33059238 DOI: 10.1016/j.compbiomed.2020.104036] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/23/2020] [Accepted: 10/03/2020] [Indexed: 12/15/2022]
Abstract
The automatic segmentation of breast tumors in ultrasound (BUS) has recently been addressed using convolutional neural networks (CNN). These CNN-based approaches generally modify a previously proposed CNN architecture or they design a new architecture using CNN ensembles. Although these methods have reported satisfactory results, the trained CNN architectures are often unavailable for reproducibility purposes. Moreover, these methods commonly learn from small BUS datasets with particular properties, which limits generalization in new cases. This paper evaluates four public CNN-based semantic segmentation models that were developed by the computer vision community, as follows: (1) Fully Convolutional Network (FCN) with AlexNet network, (2) U-Net network, (3) SegNet using VGG16 and VGG19 networks, and (4) DeepLabV3+ using ResNet18, ResNet50, MobileNet-V2, and Xception networks. By transfer learning, these CNNs are fine-tuned to segment BUS images in normal and tumoral pixels. The goal is to select a potential CNN-based segmentation model to be further used in computer-aided diagnosis (CAD) systems. The main significance of this study is the comparison of eight well-established CNN architectures using a more extensive BUS dataset than those used by approaches that are currently found in the literature. More than 3000 BUS images acquired from seven US machine models are used for training and validation. The F1-score (F1s) and the Intersection over Union (IoU) quantify the segmentation performance. The segmentation models based on SegNet and DeepLabV3+ obtain the best results with F1s>0.90 and IoU>0.81. In the case of U-Net, the segmentation performance is F1s=0.89 and IoU=0.80, whereas FCN-AlexNet attains the lowest results with F1s=0.84 and IoU=0.73. In particular, ResNet18 obtains F1s=0.905 and IoU=0.827 and requires less training time among SegNet and DeepLabV3+ networks. Hence, ResNet18 is a potential candidate for implementing fully automated end-to-end CAD systems. The CNN models generated in this study are available to researchers at https://github.com/wgomezf/CNN-BUS-segment, which attempts to impact the fair comparison with other CNN-based segmentation approaches for BUS images.
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Affiliation(s)
- Wilfrido Gómez-Flores
- Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Tamaulipas, Ciudad Victoria, Tamaulipas, Mexico.
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