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Kondejkar T, Al-Heejawi SMA, Breggia A, Ahmad B, Christman R, Ryan ST, Amal S. Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset. Bioengineering (Basel) 2024; 11:624. [PMID: 38927860 PMCID: PMC11200755 DOI: 10.3390/bioengineering11060624] [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: 05/06/2024] [Revised: 06/03/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care.
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Affiliation(s)
- Tanaya Kondejkar
- College of Engineering, Northeastern University, Boston, MA 02115, USA; (T.K.); (S.M.A.A.-H.)
| | | | - Anne Breggia
- MaineHealth Institute for Research, Scarborough, ME 04074, USA;
| | - Bilal Ahmad
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Robert Christman
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Stephen T. Ryan
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Saeed Amal
- The Roux Institute, Department of Bioengineering, College of Engineering, Northeastern University, Boston, MA 02115, USA
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2
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Balasubramanian AA, Al-Heejawi SMA, Singh A, Breggia A, Ahmad B, Christman R, Ryan ST, Amal S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers (Basel) 2024; 16:2222. [PMID: 38927927 PMCID: PMC11201924 DOI: 10.3390/cancers16122222] [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: 05/09/2024] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.
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Affiliation(s)
| | | | - Akarsh Singh
- College of Engineering, Northeastern University, Boston, MA 02115, USA; (S.M.A.A.-H.); (A.S.)
| | - Anne Breggia
- MaineHealth Institute for Research, Scarborough, ME 04074, USA;
| | - Bilal Ahmad
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Robert Christman
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Stephen T. Ryan
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Saeed Amal
- The Roux Institute, Department of Bioengineering, College of Engineering, Northeastern University, Boston, MA 02115, USA
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3
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Fang M, Fu M, Liao B, Lei X, Wu FX. Deep integrated fusion of local and global features for cervical cell classification. Comput Biol Med 2024; 171:108153. [PMID: 38364660 DOI: 10.1016/j.compbiomed.2024.108153] [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: 09/14/2023] [Revised: 02/08/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
Cervical cytology image classification is of great significance to the cervical cancer diagnosis and prognosis. Recently, convolutional neural network (CNN) and visual transformer have been adopted as two branches to learn the features for image classification by simply adding local and global features. However, such the simple addition may not be effective to integrate these features. In this study, we explore the synergy of local and global features for cytology images for classification tasks. Specifically, we design a Deep Integrated Feature Fusion (DIFF) block to synergize local and global features of cytology images from a CNN branch and a transformer branch. Our proposed method is evaluated on three cervical cell image datasets (SIPaKMeD, CRIC, Herlev) and another large blood cell dataset BCCD for several multi-class and binary classification tasks. Experimental results demonstrate the effectiveness of the proposed method in cervical cell classification, which could assist medical specialists to better diagnose cervical cancer.
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Affiliation(s)
- Ming Fang
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada
| | - Minghan Fu
- Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, 99 Longkun South Road, Haikou, 571158, Hainan, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, 620 West Chang'an Avenue, Xi'an, 710119, Shaanxi, China.
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada; Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada; Department of Computer Science, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada.
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4
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Traditional machine learning algorithms for breast cancer image classification with optimized deep features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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5
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Yusoff M, Haryanto T, Suhartanto H, Mustafa WA, Zain JM, Kusmardi K. Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review. Diagnostics (Basel) 2023; 13:diagnostics13040683. [PMID: 36832171 PMCID: PMC9955565 DOI: 10.3390/diagnostics13040683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.
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Affiliation(s)
- Marina Yusoff
- Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
- College of Computing, Informatic and Media, Kompleks Al-Khawarizmi, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
- Correspondence: (M.Y.); (W.A.M.)
| | - Toto Haryanto
- Department of Computer Science, IPB University, Bogor 16680, Indonesia
| | - Heru Suhartanto
- Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Perlis, Malaysia
- Correspondence: (M.Y.); (W.A.M.)
| | - Jasni Mohamad Zain
- Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
- College of Computing, Informatic and Media, Kompleks Al-Khawarizmi, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
| | - Kusmardi Kusmardi
- Department of Anatomical Pathology, Faculty of Medicine, Universitas Indonesia/Cipto Mangunkusumo Hospital, Jakarta 10430, Indonesia
- Human Cancer Research Cluster, Indonesia Medical Education and Research Institute, Universitas Indonesia, Jakarta 10430, Indonesia
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Jakhar AK, Gupta A, Singh M. SELF: a stacked-based ensemble learning framework for breast cancer classification. EVOLUTIONARY INTELLIGENCE 2023. [DOI: 10.1007/s12065-023-00824-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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7
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Breast cancer classification by a new approach to assessing deep neural network-based uncertainty quantification methods. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Li G, Wu G, Xu G, Li C, Zhu Z, Ye Y, Zhang H. Pathological image classification via embedded fusion mutual learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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9
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Nakach FZ, Zerouaoui H, Idri A. Hybrid deep boosting ensembles for histopathological breast cancer classification. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00709-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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10
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Breast cancer image analysis using deep learning techniques – a survey. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00703-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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11
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Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9086060. [PMID: 36262625 PMCID: PMC9576372 DOI: 10.1155/2022/9086060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/21/2022] [Accepted: 08/29/2022] [Indexed: 11/20/2022]
Abstract
Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathologists and resulting in more accurate and efficient histopathological diagnoses. Breast cancer is one of the most diagnosed cancers in women worldwide. Breast cancer may be detected and diagnosed using imaging methods such as histopathological images. Since various tissues make up the breast, there is a wide range of textural intensity, making abnormality detection difficult. As a result, there is an urgent need to improve computer-assisted systems (CAD) that can serve as a second opinion for radiologists when they use medical images. A self-training learning method employing deep learning neural network with residual learning is proposed to overcome the issue of needing a large number of labeled images to train deep learning models in breast cancer histopathology image classification. The suggested model is built from scratch and trained.
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12
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Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8904768. [PMID: 36262621 PMCID: PMC9576400 DOI: 10.1155/2022/8904768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/19/2022] [Accepted: 07/30/2022] [Indexed: 11/22/2022]
Abstract
Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.
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El-Ateif S, Idri A. Single-modality and joint fusion deep learning for diabetic retinopathy diagnosis. SCIENTIFIC AFRICAN 2022. [DOI: 10.1016/j.sciaf.2022.e01280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
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14
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Baghdadi NA, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. Classification of breast cancer using a manta-ray foraging optimized transfer learning framework. PeerJ Comput Sci 2022; 8:e1054. [PMID: 36092017 PMCID: PMC9454783 DOI: 10.7717/peerj-cs.1054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
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Affiliation(s)
- Nadiah A. Baghdadi
- College of Nursing, Nursing Management and Education Department, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amer Malki
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - Hossam Magdy Balaha
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Yousry AbdulAzeem
- Computer Engineering Department, Misr Higher Institute for Engineering and Technology, Mansoura, Egypt
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mostafa Elhosseini
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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