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Kiran A, Ramesh JVN, Rahat IS, Khan MAU, Hossain A, Uddin R. Advancing breast ultrasound diagnostics through hybrid deep learning models. Comput Biol Med 2024; 180:108962. [PMID: 39142222 DOI: 10.1016/j.compbiomed.2024.108962] [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: 05/15/2024] [Revised: 07/26/2024] [Accepted: 07/26/2024] [Indexed: 08/16/2024]
Abstract
Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the "EfficientKNN" model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm. Initially, EfficientNetB3, pre-trained on ImageNet, is repurposed to serve as a feature extractor. Subsequently, a GlobalAveragePooling2D layer is applied, followed by an optional Principal Component Analysis (PCA) to reduce dimensionality while preserving critical information. PCA is used selectively when deemed necessary. The extracted features are then classified using an optimized k-NN algorithm, fine-tuned through meticulous cross-validation.Our model underwent rigorous training using a curated dataset containing benign, malignant, and normal medical images. Data augmentation techniques, including rotations, shifts, flips, and zooms, were employed to help the model generalize and efficiently handle new, unseen data. To enhance the model's ability to identify the important features necessary for accurate predictions, the dataset was refined using segmentation and overlay techniques. The training utilized an ensemble of optimization algorithms-SGD, Adam, and RMSprop-with hyperparameters set at a learning rate of 0.00045, a batch size of 32, and up to 120 epochs, facilitated by early stopping to prevent overfitting.The results demonstrate that the EfficientKNN model outperforms traditional models such as VGG16, AlexNet, and VGG19 in terms of accuracy, precision, and F1-score. Additionally, the model showed better results compared to EfficientNetB3 alone. Achieving a 100 % accuracy rate on multiple tests, the EfficientKNN model has significant potential for real-world diagnostic applications. This study highlights the model's scalability, efficient use of cloud storage, and real-time prediction capabilities, all while minimizing computational demands.By integrating the strengths of EfficientNetB3's deep learning architecture with the interpretability of k-NN, EfficientKNN presents a significant advancement in medical image classification, promising improved diagnostic accuracy and clinical applicability.
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Affiliation(s)
- Ajmeera Kiran
- Department of Computer Science and Engineering,MLR Institute of Technology, Dundigal, Hyderabad, Telangana, 500043, India
| | - Janjhyam Venkata Naga Ramesh
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India; Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, 248002, India
| | - Irfan Sadiq Rahat
- School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India.
| | | | - Anwar Hossain
- Master Of Information Science and TechnologyCalifornia State University, USA
| | - Roise Uddin
- Master Of Information Science and TechnologyCalifornia State University, USA
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2
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Islam R, Tarique M. Artificial Intelligence (AI) and Nuclear Features from the Fine Needle Aspirated (FNA) Tissue Samples to Recognize Breast Cancer. J Imaging 2024; 10:201. [PMID: 39194990 DOI: 10.3390/jimaging10080201] [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: 07/27/2024] [Revised: 08/14/2024] [Accepted: 08/16/2024] [Indexed: 08/29/2024] Open
Abstract
Breast cancer is one of the paramount causes of new cancer cases worldwide annually. It is a malignant neoplasm that develops in the breast cells. The early screening of this disease is essential to prevent its metastasis. A mammogram X-ray image is the most common screening tool practiced currently when this disease is suspected; all the breast lesions identified are not malignant. The invasive fine needle aspiration (FNA) of a breast mass sample is the secondary screening tool to clinically examine cancerous lesions. The visual image analysis of the stained aspirated sample imposes a challenge for the cytologist to identify the malignant cells accurately. The formulation of an artificial intelligence-based objective technique on top of the introspective assessment is essential to avoid misdiagnosis. This paper addresses several artificial intelligence (AI)-based techniques to diagnose breast cancer from the nuclear features of FNA samples. The Wisconsin Breast Cancer dataset (WBCD) from the UCI machine learning repository is applied for this investigation. Significant statistical parameters are measured to evaluate the performance of the proposed techniques. The best detection accuracy of 98.10% is achieved with a two-layer feed-forward neural network (FFNN). Finally, the developed algorithm's performance is compared with some state-of-the-art works in the literature.
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Affiliation(s)
- Rumana Islam
- Department of Electrical and Computer Engineering, University of Science and Technology of Fujairah (USTF), Fujairah P.O. Box 2202, United Arab Emirates
- Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada
| | - Mohammed Tarique
- Department of Electrical and Computer Engineering, University of Science and Technology of Fujairah (USTF), Fujairah P.O. Box 2202, United Arab Emirates
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3
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Wang H, Wang T, Hao Y, Ding S, Feng J. Breast tumor segmentation via deep correlation analysis of multi-sequence MRI. Med Biol Eng Comput 2024:10.1007/s11517-024-03166-0. [PMID: 39031329 DOI: 10.1007/s11517-024-03166-0] [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: 12/19/2023] [Accepted: 07/03/2024] [Indexed: 07/22/2024]
Abstract
Precise segmentation of breast tumors from MRI is crucial for breast cancer diagnosis, as it allows for detailed calculation of tumor characteristics such as shape, size, and edges. Current segmentation methodologies face significant challenges in accurately modeling the complex interrelationships inherent in multi-sequence MRI data. This paper presents a hybrid deep network framework with three interconnected modules, aimed at efficiently integrating and exploiting the spatial-temporal features among multiple MRI sequences for breast tumor segmentation. The first module involves an advanced multi-sequence encoder with a densely connected architecture, separating the encoding pathway into multiple streams for individual MRI sequences. To harness the intricate correlations between different sequence features, we propose a sequence-awareness and temporal-awareness method that adeptly fuses spatial-temporal features of MRI in the second multi-scale feature embedding module. Finally, the decoder module engages in the upsampling of feature maps, meticulously refining the resolution to achieve highly precise segmentation of breast tumors. In contrast to other popular methods, the proposed method learns the interrelationships inherent in multi-sequence MRI. We justify the proposed method through extensive experiments. It achieves notable improvements in segmentation performance, with Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Positive Predictive Value (PPV) scores of 80.57%, 74.08%, and 84.74% respectively.
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Affiliation(s)
- Hongyu Wang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China.
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China.
- Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China.
| | - Tonghui Wang
- Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi, 7101127, China
| | - Yanfang Hao
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China
- Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China
| | - Songtao Ding
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China
- Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, 710121, China
| | - Jun Feng
- Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi, 7101127, China.
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4
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Aldakhil LA, Alhasson HF, Alharbi SS. Attention-Based Deep Learning Approach for Breast Cancer Histopathological Image Multi-Classification. Diagnostics (Basel) 2024; 14:1402. [PMID: 39001292 PMCID: PMC11241245 DOI: 10.3390/diagnostics14131402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/18/2024] [Accepted: 06/18/2024] [Indexed: 07/16/2024] Open
Abstract
Breast cancer diagnosis from histopathology images is often time consuming and prone to human error, impacting treatment and prognosis. Deep learning diagnostic methods offer the potential for improved accuracy and efficiency in breast cancer detection and classification. However, they struggle with limited data and subtle variations within and between cancer types. Attention mechanisms provide feature refinement capabilities that have shown promise in overcoming such challenges. To this end, this paper proposes the Efficient Channel Spatial Attention Network (ECSAnet), an architecture built on EfficientNetV2 and augmented with a convolutional block attention module (CBAM) and additional fully connected layers. ECSAnet was fine-tuned using the BreakHis dataset, employing Reinhard stain normalization and image augmentation techniques to minimize overfitting and enhance generalizability. In testing, ECSAnet outperformed AlexNet, DenseNet121, EfficientNetV2-S, InceptionNetV3, ResNet50, and VGG16 in most settings, achieving accuracies of 94.2% at 40×, 92.96% at 100×, 88.41% at 200×, and 89.42% at 400× magnifications. The results highlight the effectiveness of CBAM in improving classification accuracy and the importance of stain normalization for generalizability.
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Affiliation(s)
| | - Haifa F. Alhasson
- Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia; (L.A.A.); (S.S.A.)
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Valdez D, Bunnell A, Lim SY, Sadowski P, Shepherd JA. Performance of Progressive Generations of GPT on an Exam Designed for Certifying Physicians as Certified Clinical Densitometrists. J Clin Densitom 2024; 27:101480. [PMID: 38401238 DOI: 10.1016/j.jocd.2024.101480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/25/2024] [Accepted: 02/15/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Artificial intelligence (AI) large language models (LLMs) such as ChatGPT have demonstrated the ability to pass standardized exams. These models are not trained for a specific task, but instead trained to predict sequences of text from large corpora of documents sourced from the internet. It has been shown that even models trained on this general task can pass exams in a variety of domain-specific fields, including the United States Medical Licensing Examination. We asked if large language models would perform as well on a much narrower subdomain tests designed for medical specialists. Furthermore, we wanted to better understand how progressive generations of GPT (generative pre-trained transformer) models may be evolving in the completeness and sophistication of their responses even while generational training remains general. In this study, we evaluated the performance of two versions of GPT (GPT 3 and 4) on their ability to pass the certification exam given to physicians to work as osteoporosis specialists and become a certified clinical densitometrists. The CCD exam has a possible score range of 150 to 400. To pass, you need a score of 300. METHODS A 100-question multiple-choice practice exam was obtained from a 3rd party exam preparation website that mimics the accredited certification tests given by the ISCD (International Society for Clinical Densitometry). The exam was administered to two versions of GPT, the free version (GPT Playground) and ChatGPT+, which are based on GPT-3 and GPT-4, respectively (OpenAI, San Francisco, CA). The systems were prompted with the exam questions verbatim. If the response was purely textual and did not specify which of the multiple-choice answers to select, the authors matched the text to the closest answer. Each exam was graded and an estimated ISCD score was provided from the exam website. In addition, each response was evaluated by a rheumatologist CCD and ranked for accuracy using a 5-level scale. The two GPT versions were compared in terms of response accuracy and length. RESULTS The average response length was 11.6 ±19 words for GPT-3 and 50.0±43.6 words for GPT-4. GPT-3 answered 62 questions correctly resulting in a failing ISCD score of 289. However, GPT-4 answered 82 questions correctly with a passing score of 342. GPT-3 scored highest on the "Overview of Low Bone Mass and Osteoporosis" category (72 % correct) while GPT-4 scored well above 80 % accuracy on all categories except "Imaging Technology in Bone Health" (65 % correct). Regarding subjective accuracy, GPT-3 answered 23 questions with nonsensical or totally wrong responses while GPT-4 had no responses in that category. CONCLUSION If this had been an actual certification exam, GPT-4 would now have a CCD suffix to its name even after being trained using general internet knowledge. Clearly, more goes into physician training than can be captured in this exam. However, GPT algorithms may prove to be valuable physician aids in the diagnoses and monitoring of osteoporosis and other diseases.
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Affiliation(s)
- Dustin Valdez
- University of Hawaii at Manoa, Honolulu, HI, USA; University of Hawaii Cancer Center, Honolulu, HI, USA.
| | - Arianna Bunnell
- University of Hawaii at Manoa, Honolulu, HI, USA; University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Sian Y Lim
- Hawai'i Pacific Health Medical Group, Hawai'i Pacific Health, Honolulu, HI, USA
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Lam ST, Lam JW, Reddy AJ, Lee L, Yu Z, Falkenstein BE, Fu VW, Cheng E, Patel R. Advancing Breast Cancer Research Through Collaborative Computing: Harnessing Google Colab for Innovation. Cureus 2024; 16:e57280. [PMID: 38690491 PMCID: PMC11058570 DOI: 10.7759/cureus.57280] [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] [Accepted: 03/30/2024] [Indexed: 05/02/2024] Open
Abstract
This investigation explores the potential efficacy of machine learning algorithms (MLAs), particularly convolutional neural networks (CNNs), in distinguishing between benign and malignant breast cancer tissue through the analysis of 1000 breast cancer images gathered from Kaggle.com, a domain of publicly accessible data. The dataset was meticulously partitioned into training, validation, and testing sets to facilitate model development and evaluation. Our results reveal promising outcomes, with the developed model achieving notable precision (92%), recall (92%), accuracy (92%), sensitivity (89%), specificity (96%), an F1 score of 0.92, and an area under the curve (AUC) of 0.944. These metrics underscore the model's ability to accurately identify malignant breast cancer images. Because of limitations such as sample size and potential variations in image quality, further research, data collection, and integration of theoretical models in a real-world clinical setting are needed to expand the reliability and generalizability of these MLAs. Nonetheless, this study serves to highlight the potential use of artificial intelligence models as supporting tools for physicians to utilize in breast cancer detection.
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Affiliation(s)
- Sydney T Lam
- Medicine, California University of Science and Medicine, Colton, USA
| | - Jonathan W Lam
- Medicine, California University of Science and Medicine, Colton, USA
| | - Akshay J Reddy
- Medicine, California University of Science and Medicine, Colton, USA
| | - Longines Lee
- Medicine, California University of Science and Medicine, Colton, USA
| | - Zeyu Yu
- Medicine, California Health Sciences University, Clovis, USA
| | | | - Victor W Fu
- Medicine, California Health Sciences University, Clovis, USA
| | - Evan Cheng
- Medicine, California Health Sciences University, Clovis, USA
| | - Rakesh Patel
- Internal Medicine, Quillen College of Medicine, East Tennessee State University, Johnson City, USA
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Rakhshaninejad M, Fathian M, Shirkoohi R, Barzinpour F, Gandomi AH. Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach. BMC Bioinformatics 2024; 25:33. [PMID: 38253993 PMCID: PMC10810249 DOI: 10.1186/s12859-024-05657-1] [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: 12/03/2023] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.
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Affiliation(s)
- Morteza Rakhshaninejad
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran
| | - Mohammad Fathian
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran.
| | - Reza Shirkoohi
- Cancer Biology Research Center, Cancer Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Keshavarz Boulevard, Tehran, 1419733141, Tehran, Iran
| | - Farnaz Barzinpour
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, 2007, NSW, Australia
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary
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Zadeh Shirazi A, Tofighi M, Gharavi A, Gomez GA. The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide. Technol Cancer Res Treat 2024; 23:15330338241250324. [PMID: 38775067 PMCID: PMC11113055 DOI: 10.1177/15330338241250324] [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: 10/29/2023] [Revised: 03/28/2024] [Accepted: 04/08/2024] [Indexed: 05/25/2024] Open
Abstract
Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.
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Affiliation(s)
- Amin Zadeh Shirazi
- Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, SA, Australia
| | - Morteza Tofighi
- Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
| | - Alireza Gharavi
- Department of Computer Science, Azad University, Mashhad Branch, Mashhad, Iran
| | - Guillermo A. Gomez
- Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, SA, Australia
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [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: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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Dank G, Buber T, Rice A, Kraicer N, Hanael E, Shasha T, Aviram G, Yehudayoff A, Kent MS. Training and validation of a novel non-invasive imaging system for ruling out malignancy in canine subcutaneous and cutaneous masses using machine learning in 664 masses. Front Vet Sci 2023; 10:1164438. [PMID: 37841459 PMCID: PMC10570610 DOI: 10.3389/fvets.2023.1164438] [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: 02/12/2023] [Accepted: 09/05/2023] [Indexed: 10/17/2023] Open
Abstract
Objective To train and validate the use of a novel artificial intelligence-based thermal imaging system as a screening tool to rule out malignancy in cutaneous and subcutaneous masses in dogs. Animals Training study: 147 client-owned dogs with 233 masses. Validation Study: 299 client-owned dogs with 525 masses. Cytology was non-diagnostic in 94 masses, resulting in 431 masses from 248 dogs with diagnostic samples. Procedures The prospective studies were conducted between June 2020 and July 2022. During the scan, each mass and its adjacent healthy tissue was heated by a high-power Light-Emitting Diode. The tissue temperature was recorded by the device and consequently analyzed using a supervised machine learning algorithm to determine whether the mass required further investigation. The first study was performed to collect data to train the algorithm. The second study validated the algorithm, as the real-time device predictions were compared to the cytology and/or biopsy results. Results The results for the validation study were that the device correctly classified 45 out of 53 malignant masses and 253 out of 378 benign masses (sensitivity = 85% and specificity = 67%). The negative predictive value of the system (i.e., percent of benign masses identified as benign) was 97%. Clinical relevance The results demonstrate that this novel system could be used as a decision-support tool at the point of care, enabling clinicians to differentiate between benign lesions and those requiring further diagnostics.
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Affiliation(s)
| | - Tali Buber
- HT BioImaging Ltd., Hod Hasharon, Israel
| | - Anna Rice
- HT BioImaging Ltd., Hod Hasharon, Israel
| | | | | | | | - Gal Aviram
- Department Biomedical Engineering, Tel Aviv University, Tel Aviv-Yafo, Israel
| | | | - Michael S. Kent
- Department of Surgical and Radiological Sciences, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
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Bibi S, Khan MA, Shah JH, Damaševičius R, Alasiry A, Marzougui M, Alhaisoni M, Masood A. MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection. Diagnostics (Basel) 2023; 13:3063. [PMID: 37835807 PMCID: PMC10572512 DOI: 10.3390/diagnostics13193063] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/19/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
Cancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection to receive immediate and successful treatment. Lesion detection and classification are more challenging due to many forms of artifacts such as hairs, noise, and irregularity of lesion shape, color, irrelevant features, and textures. In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection. The proposed architecture consists of four core steps: image preprocessing, feature extraction and fusion, feature selection, and classification. A novel contrast enhancement technique is proposed based on the image luminance information. After that, two pre-trained deep models, DarkNet-53 and DensNet-201, are modified in terms of a residual block at the end and trained through transfer learning. In the learning process, the Genetic algorithm is applied to select hyperparameters. The resultant features are fused using a two-step approach named serial-harmonic mean. This step increases the accuracy of the correct classification, but some irrelevant information is also observed. Therefore, an algorithm is developed to select the best features called marine predator optimization (MPA) controlled Reyni Entropy. The selected features are finally classified using machine learning classifiers for the final classification. Two datasets, ISIC2018 and ISIC2019, have been selected for the experimental process. On these datasets, the obtained maximum accuracy of 85.4% and 98.80%, respectively. To prove the effectiveness of the proposed methods, a detailed comparison is conducted with several recent techniques and shows the proposed framework outperforms.
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Affiliation(s)
- Sobia Bibi
- Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad 45550, Pakistan; (S.B.); (J.H.S.)
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut 1102-2801, Lebanon;
- Department of CS, HITEC University, Taxila 47080, Pakistan
| | - Jamal Hussain Shah
- Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad 45550, Pakistan; (S.B.); (J.H.S.)
| | - Robertas Damaševičius
- Center of Excellence Forest 4.0, Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania;
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; (A.A.); (M.M.)
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; (A.A.); (M.M.)
| | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia;
| | - Anum Masood
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
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Rangel G, Cuevas-Tello JC, Rivera M, Renteria O. A Deep Learning Model Based on Capsule Networks for COVID Diagnostics through X-ray Images. Diagnostics (Basel) 2023; 13:2858. [PMID: 37685396 PMCID: PMC10486517 DOI: 10.3390/diagnostics13172858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
X-ray diagnostics are widely used to detect various diseases, such as bone fracture, pneumonia, or intracranial hemorrhage. This method is simple and accessible in most hospitals, but requires an expert who is sometimes unavailable. Today, some diagnoses are made with the help of deep learning algorithms based on Convolutional Neural Networks (CNN), but these algorithms show limitations. Recently, Capsule Networks (CapsNet) have been proposed to overcome these problems. In our work, CapsNet is used to detect whether a chest X-ray image has disease (COVID or pneumonia) or is healthy. An improved model called DRCaps is proposed, which combines the advantage of CapsNet and the dilation rate (dr) parameter to manage images with 226 × 226 resolution. We performed experiments with 16,669 chest images, in which our model achieved an accuracy of 90%. Furthermore, the model size is 11M with a reconstruction stage, which helps to avoid overfitting. Experiments show how the reconstruction stage works and how we can avoid the max-pooling operation for networks with a stride and dilation rate to downsampling the convolution layers. In this paper, DRCaps is superior to other comparable models in terms of accuracy, parameters, and image size handling. The main idea is to keep the model as simple as possible without using data augmentation or a complex preprocessing stage.
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Affiliation(s)
- Gabriela Rangel
- Facultad de Ingeniería, Universidad Autonoma de San Luis Potosi, San Luis Potosi 78290, Mexico;
- Tecnologico Nacional de Mexico/ITSSLPC, San Luis Potosi 78421, Mexico
| | - Juan C. Cuevas-Tello
- Facultad de Ingeniería, Universidad Autonoma de San Luis Potosi, San Luis Potosi 78290, Mexico;
| | - Mariano Rivera
- Centro de Investigacion en Matematicas, Guanajuato 36000, Mexico; (M.R.); (O.R.)
| | - Octavio Renteria
- Centro de Investigacion en Matematicas, Guanajuato 36000, Mexico; (M.R.); (O.R.)
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Liu H, Hou CJ, Tang JL, Sun LT, Lu KF, Liu Y, Du P. Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images. Sci Rep 2023; 13:10500. [PMID: 37380667 DOI: 10.1038/s41598-023-37319-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 06/20/2023] [Indexed: 06/30/2023] Open
Abstract
This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.
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Affiliation(s)
- Han Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Chun-Jie Hou
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Jing-Lan Tang
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China.
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China.
| | - Li-Tao Sun
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Ke-Feng Lu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Ying Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Pei Du
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
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Ke X, Liu W, Shen L, Zhang Y, Liu W, Wang C, Wang X. Early Screening of Colorectal Precancerous Lesions Based on Combined Measurement of Multiple Serum Tumor Markers Using Artificial Neural Network Analysis. BIOSENSORS 2023; 13:685. [PMID: 37504084 PMCID: PMC10377288 DOI: 10.3390/bios13070685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/21/2023] [Accepted: 06/24/2023] [Indexed: 07/29/2023]
Abstract
Many patients with colorectal cancer (CRC) are diagnosed in the advanced stage, resulting in delayed treatment and reduced survival time. It is urgent to develop accurate early screening methods for CRC. The purpose of this study is to develop an artificial intelligence (AI)-based artificial neural network (ANN) model using multiple protein tumor markers to assist in the early diagnosis of CRC and precancerous lesions. In this retrospective analysis, 148 cases with CRC and precancerous diseases were included. The concentrations of multiple protein tumor markers (CEA, CA19-9, CA 125, CYFRA 21-1, CA 72-4, CA 242) were measured by electrochemical luminescence immunoassays. By combining these markers with an ANN algorithm, a diagnosis model (CA6) was developed to distinguish between normal healthy and abnormal subjects, with an AUC of 0.97. The prediction score derived from the CA6 model also performed well in assisting in the diagnosis of precancerous lesions and early CRC (with AUCs of 0.97 and 0.93 and cut-off values of 0.39 and 0.34, respectively), which was better than that of individual protein tumor indicators. The CA6 model established by ANN provides a new and effective method for laboratory auxiliary diagnosis, which might be utilized for early colorectal lesion screening by incorporating more tumor markers with larger sample size.
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Affiliation(s)
- Xing Ke
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai 200092, China
| | - Wenxue Liu
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Lisong Shen
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai 200092, China
| | - Yue Zhang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Wei Liu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing 100080, China
| | - Chaofu Wang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xu Wang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Nanning Jiuzhouyuan Biotechnology Co., Ltd., Nanning 530007, China
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Mirza Z, Ansari MS, Iqbal MS, Ahmad N, Alganmi N, Banjar H, Al-Qahtani MH, Karim S. Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis. Cancers (Basel) 2023; 15:3237. [PMID: 37370847 DOI: 10.3390/cancers15123237] [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: 05/15/2023] [Revised: 06/10/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. METHODS A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. RESULTS We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. CONCLUSION The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.
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Affiliation(s)
- Zeenat Mirza
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Md Shahid Ansari
- Department of Clinical Data Analytics, Max Super Speciality Hospital, Saket, New Delhi 110017, India
| | - Md Shahid Iqbal
- Department of Statistics and Computer Applications, Tilka Manjhi Bhagalpur University, Bhagalpur 812007, India
| | - Nesar Ahmad
- Department of Statistics and Computer Applications, Tilka Manjhi Bhagalpur University, Bhagalpur 812007, India
| | - Nofe Alganmi
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Haneen Banjar
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohammed H Al-Qahtani
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Sajjad Karim
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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