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Li W, Guo E, Zhao H, Li Y, Miao L, Liu C, Sun W. Evaluation of transfer ensemble learning-based convolutional neural network models for the identification of chronic gingivitis from oral photographs. BMC Oral Health 2024; 24:814. [PMID: 39020332 PMCID: PMC11256452 DOI: 10.1186/s12903-024-04460-x] [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: 01/23/2024] [Accepted: 06/07/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND To evaluate the performances of several advanced deep convolutional neural network models (AlexNet, VGG, GoogLeNet, ResNet) based on ensemble learning for recognizing chronic gingivitis from screening oral images. METHODS A total of 683 intraoral clinical images acquired from 134 volunteers were used to construct the database and evaluate the models. Four deep ConvNet models were developed using ensemble learning and outperformed a single model. The performances of the different models were evaluated by comparing the accuracy and sensitivity for recognizing the existence of gingivitis from intraoral images. RESULTS The ResNet model achieved an area under the curve (AUC) value of 97%, while the AUC values for the GoogLeNet, AlexNet, and VGG models were 94%, 92%, and 89%, respectively. Although the ResNet and GoogLeNet models performed best in classifying gingivitis from images, the sensitivity outcomes were not significantly different among the ResNet, GoogLeNet, and Alexnet models (p>0.05). However, the sensitivity of the VGGNet model differed significantly from those of the other models (p < 0.001). CONCLUSION The ResNet and GoogLeNet models show promise for identifying chronic gingivitis from images. These models can help doctors diagnose periodontal diseases efficiently or based on self-examination of the oral cavity by patients.
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Affiliation(s)
- Wen Li
- Department of Cariology and Endodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China
| | - Enting Guo
- Division of Computer Science, The University of Aizu, Aizu, Japan
| | - Hong Zhao
- Division of Computer Science, The University of Aizu, Aizu, Japan
| | - Yuyang Li
- Department of Cariology and Endodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China
| | - Leiying Miao
- Department of Cariology and Endodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China
| | - Chao Liu
- Department of Orthodontic, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China.
| | - Weibin Sun
- Department of Periodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China.
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Ramamoorthy P, Ramakantha Reddy BR, Askar SS, Abouhawwash M. Histopathology-based breast cancer prediction using deep learning methods for healthcare applications. Front Oncol 2024; 14:1300997. [PMID: 38894870 PMCID: PMC11184215 DOI: 10.3389/fonc.2024.1300997] [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: 10/01/2023] [Accepted: 04/12/2024] [Indexed: 06/21/2024] Open
Abstract
Breast cancer (BC) is the leading cause of female cancer mortality and is a type of cancer that is a major threat to women's health. Deep learning methods have been used extensively in many medical domains recently, especially in detection and classification applications. Studying histological images for the automatic diagnosis of BC is important for patients and their prognosis. Owing to the complication and variety of histology images, manual examination can be difficult and susceptible to errors and thus needs the services of experienced pathologists. Therefore, publicly accessible datasets called BreakHis and invasive ductal carcinoma (IDC) are used in this study to analyze histopathological images of BC. Next, using super-resolution generative adversarial networks (SRGANs), which create high-resolution images from low-quality images, the gathered images from BreakHis and IDC are pre-processed to provide useful results in the prediction stage. The components of conventional generative adversarial network (GAN) loss functions and effective sub-pixel nets were combined to create the concept of SRGAN. Next, the high-quality images are sent to the data augmentation stage, where new data points are created by making small adjustments to the dataset using rotation, random cropping, mirroring, and color-shifting. Next, patch-based feature extraction using Inception V3 and Resnet-50 (PFE-INC-RES) is employed to extract the features from the augmentation. After the features have been extracted, the next step involves processing them and applying transductive long short-term memory (TLSTM) to improve classification accuracy by decreasing the number of false positives. The results of suggested PFE-INC-RES is evaluated using existing methods on the BreakHis dataset, with respect to accuracy (99.84%), specificity (99.71%), sensitivity (99.78%), and F1-score (99.80%), while the suggested PFE-INC-RES performed better in the IDC dataset based on F1-score (99.08%), accuracy (99.79%), specificity (98.97%), and sensitivity (99.17%).
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Affiliation(s)
- Prabhu Ramamoorthy
- Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, India
| | | | - S. S. Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, Egypt
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Silva AB, Martins AS, Tosta TAA, Loyola AM, Cardoso SV, Neves LA, de Faria PR, do Nascimento MZ. OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01041-w. [PMID: 38409608 DOI: 10.1007/s10278-024-01041-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/28/2024]
Abstract
Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia, is the most reliable way to prevent oral cancer. Computational algorithms have been used as an auxiliary tool to aid specialists in this process. Usually, experiments are performed on private data, making it difficult to reproduce the results. There are several public datasets of histological images, but studies focused on oral dysplasia images use inaccessible datasets. This prevents the improvement of algorithms aimed at this lesion. This study introduces an annotated public dataset of oral epithelial dysplasia tissue images. The dataset includes 456 images acquired from 30 mouse tongues. The images were categorized among the lesion grades, with nuclear structures manually marked by a trained specialist and validated by a pathologist. Also, experiments were carried out in order to illustrate the potential of the proposed dataset in classification and segmentation processes commonly explored in the literature. Convolutional neural network (CNN) models for semantic and instance segmentation were employed on the images, which were pre-processed with stain normalization methods. Then, the segmented and non-segmented images were classified with CNN architectures and machine learning algorithms. The data obtained through these processes is available in the dataset. The segmentation stage showed the F1-score value of 0.83, obtained with the U-Net model using the ResNet-50 as a backbone. At the classification stage, the most expressive result was achieved with the Random Forest method, with an accuracy value of 94.22%. The results show that the segmentation contributed to the classification results, but studies are needed for the improvement of these stages of automated diagnosis. The original, gold standard, normalized, and segmented images are publicly available and may be used for the improvement of clinical applications of CAD methods on oral epithelial dysplasia tissue images.
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Affiliation(s)
- Adriano Barbosa Silva
- Faculty of Computer Science (FACOM) - Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB, 38400-902, Uberlândia, MG, Brazil.
| | - Alessandro Santana Martins
- Federal Institute of Triângulo Mineiro (IFTM), R. Belarmino Vilela Junqueira, S/N, 38305-200, Ituiutaba, MG, Brazil
| | - Thaína Aparecida Azevedo Tosta
- Science and Technology Institute, Federal University of São Paulo (UNIFESP), Av. Cesare Mansueto Giulio Lattes, 1201, 12247-014, São José dos Campos, SP, Brazil
| | - Adriano Mota Loyola
- School of Dentistry, Federal University of Uberlândia (UFU), Av. Pará - 1720, 38405-320, Uberlândia, MG, Brazil
| | - Sérgio Vitorino Cardoso
- School of Dentistry, Federal University of Uberlândia (UFU), Av. Pará - 1720, 38405-320, Uberlândia, MG, Brazil
| | - Leandro Alves Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), R. Cristóvão Colombo, 2265, 38305-200, São José do Rio Preto, SP, Brazil
| | - Paulo Rogério de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Av. Amazonas, S/N, 38405-320, Uberlândia, MG, Brazil
| | - Marcelo Zanchetta do Nascimento
- Faculty of Computer Science (FACOM) - Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB, 38400-902, Uberlândia, MG, Brazil
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Albalawi E, Thakur A, Ramakrishna MT, Bhatia Khan S, SankaraNarayanan S, Almarri B, Hadi TH. Oral squamous cell carcinoma detection using EfficientNet on histopathological images. Front Med (Lausanne) 2024; 10:1349336. [PMID: 38348235 PMCID: PMC10859441 DOI: 10.3389/fmed.2023.1349336] [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: 12/04/2023] [Accepted: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Introduction Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization. Methods The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies. Results The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC. Discussion This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.
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Affiliation(s)
- Eid Albalawi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Arastu Thakur
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Mahesh Thyluru Ramakrishna
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science, Engineering and Environment, University of Salford, Salford, United Kingdom
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Suresh SankaraNarayanan
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Badar Almarri
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Theyazn Hassn Hadi
- Applied College in Abqaiq, King Faisal University, Al-Ahsa, Saudi Arabia
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5
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Nagarajan B, Chakravarthy S, Venkatesan VK, Ramakrishna MT, Khan SB, Basheer S, Albalawi E. A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma. Diagnostics (Basel) 2023; 13:3461. [PMID: 37998597 PMCID: PMC10670914 DOI: 10.3390/diagnostics13223461] [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: 10/04/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
Abstract
One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient's histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.
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Affiliation(s)
- Bharanidharan Nagarajan
- School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India; (B.N.); (V.K.V.)
| | - Sannasi Chakravarthy
- Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
| | - Vinoth Kumar Venkatesan
- School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India; (B.N.); (V.K.V.)
| | - Mahesh Thyluru Ramakrishna
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-Be University), Bangalore 562112, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UK
- Department of Engineering and Environment, University of Religions and Denominations, Qom 13357, Iran
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Eid Albalawi
- Department of Computer Science, School of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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6
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Islam MM, Alam KMR, Uddin J, Ashraf I, Samad MA. Benign and Malignant Oral Lesion Image Classification Using Fine-Tuned Transfer Learning Techniques. Diagnostics (Basel) 2023; 13:3360. [PMID: 37958257 PMCID: PMC10650377 DOI: 10.3390/diagnostics13213360] [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: 09/28/2023] [Revised: 10/23/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
Oral lesions are a prevalent manifestation of oral disease, and the timely identification of oral lesions is imperative for effective intervention. Fortunately, deep learning algorithms have shown great potential for automated lesion detection. The primary aim of this study was to employ deep learning-based image classification algorithms to identify oral lesions. We used three deep learning models, namely VGG19, DeIT, and MobileNet, to assess the efficacy of various categorization methods. To evaluate the accuracy and reliability of the models, we employed a dataset consisting of oral pictures encompassing two distinct categories: benign and malignant lesions. The experimental findings indicate that VGG19 and MobileNet attained an almost perfect accuracy rate of 100%, while DeIT achieved a slightly lower accuracy rate of 98.73%. The results of this study indicate that deep learning algorithms for picture classification demonstrate a high level of effectiveness in detecting oral lesions by achieving 100% for VGG19 and MobileNet and 98.73% for DeIT. Specifically, the VGG19 and MobileNet models exhibit notable suitability for this particular task.
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Affiliation(s)
- Md. Monirul Islam
- Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka 1216, Bangladesh
| | - K. M. Rafiqul Alam
- Department of Statistics, Jahangirnagar University, Dhaka 1342, Bangladesh
| | - Jia Uddin
- AI and Big Data Department, Endicott College, Woosong University, Daejeon 34606, Republic of Korea
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea
| | - Md Abdus Samad
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea
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7
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Ahmad M, Irfan MA, Sadique U, Haq IU, Jan A, Khattak MI, Ghadi YY, Aljuaid H. Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques. Cancers (Basel) 2023; 15:5247. [PMID: 37958422 PMCID: PMC10650156 DOI: 10.3390/cancers15215247] [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: 09/28/2023] [Revised: 10/22/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.
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Affiliation(s)
- Mehran Ahmad
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
| | - Muhammad Abeer Irfan
- Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan;
| | - Umar Sadique
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
- Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan;
| | - Ihtisham ul Haq
- AIH, Intelligent Information Processing Lab (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan; (U.S.); (I.u.H.)
- Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan
| | - Atif Jan
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
| | - Muhammad Irfan Khattak
- Department of Electrical Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan; (M.A.); (A.J.); (M.I.K.)
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Al Ain 15551, United Arab Emirates;
| | - Hanan Aljuaid
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), Riyadh 11671, Saudi Arabia
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8
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Pereira-Prado V, Martins-Silveira F, Sicco E, Hochmann J, Isiordia-Espinoza MA, González RG, Pandiar D, Bologna-Molina R. Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review. Diagnostics (Basel) 2023; 13:2416. [PMID: 37510160 PMCID: PMC10378350 DOI: 10.3390/diagnostics13142416] [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: 06/26/2023] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation.
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Affiliation(s)
- Vanesa Pereira-Prado
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Felipe Martins-Silveira
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Estafanía Sicco
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Jimena Hochmann
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
| | - Mario Alberto Isiordia-Espinoza
- Department of Clinics, Los Altos University Center, Institute of Research in Medical Sciences, University of Guadalajara, Guadalajara 44100, Mexico
| | - Rogelio González González
- Research Department, School of Dentistry, Universidad Juárez del Estado de Durango, Durango 34000, Mexico
| | - Deepak Pandiar
- Department of Oral Pathology and Microbiology, Saveetha Dental College and Hospitals, Chennai 600077, India
| | - Ronell Bologna-Molina
- Molecular Pathology Area, School of Dentistry, Universidad de la República, Montevideo 11400, Uruguay
- Research Department, School of Dentistry, Universidad Juárez del Estado de Durango, Durango 34000, Mexico
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9
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Sukegawa S, Ono S, Tanaka F, Inoue Y, Hara T, Yoshii K, Nakano K, Takabatake K, Kawai H, Katsumitsu S, Nakai F, Nakai Y, Miyazaki R, Murakami S, Nagatsuka H, Miyake M. Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists. Sci Rep 2023; 13:11676. [PMID: 37468501 DOI: 10.1038/s41598-023-38343-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 07/06/2023] [Indexed: 07/21/2023] Open
Abstract
The study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (CNN) deep learning models and shows how the results can improve diagnosis. Histopathological samples of oral squamous cell carcinoma were prepared by oral pathologists. Images were divided into tiles on a virtual slide, and labels (squamous cell carcinoma, normal, and others) were applied. VGG16 and ResNet50 with the optimizers stochastic gradient descent with momentum and spectral angle mapper (SAM) were used, with and without a learning rate scheduler. The conditions for achieving good CNN performances were identified by examining performance metrics. We used ROCAUC to statistically evaluate diagnostic performance improvement of six oral pathologists using the results from the selected CNN model for assisted diagnosis. VGG16 with SAM showed the best performance, with accuracy = 0.8622 and AUC = 0.9602. The diagnostic performances of the oral pathologists statistically significantly improved when the diagnostic results of the deep learning model were used as supplementary diagnoses (p-value = 0.031). By considering the learning results of deep learning model classifiers, the diagnostic accuracy of pathologists can be improved. This study contributes to the application of highly reliable deep learning models for oral pathological diagnosis.
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Affiliation(s)
- Shintaro Sukegawa
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan.
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-Machi, Takamatsu, Kagawa, 760-8557, Japan.
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan.
| | - Sawako Ono
- Department of Pathology, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-Machi, Takamatsu, Kagawa, 760-8557, Japan
| | - Futa Tanaka
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Yuta Inoue
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Takeshi Hara
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
- Center for Healthcare Information Technology, Tokai National Higher Education and Research System, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Kazumasa Yoshii
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Keisuke Nakano
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Kiyofumi Takabatake
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Hotaka Kawai
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Shimada Katsumitsu
- Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental University, 1780 Hirooka-Gobara, Shiojiri, Nagano, 399-0781, Japan
| | - Fumi Nakai
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Yasuhiro Nakai
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Ryo Miyazaki
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Satoshi Murakami
- Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental University, 1780 Hirooka-Gobara, Shiojiri, Nagano, 399-0781, Japan
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Minoru Miyake
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
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10
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Mohan R, Rama A, Raja RK, Shaik MR, Khan M, Shaik B, Rajinikanth V. OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection. Biomolecules 2023; 13:1090. [PMID: 37509126 PMCID: PMC10377094 DOI: 10.3390/biom13071090] [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: 04/26/2023] [Revised: 06/17/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework's performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet's validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.
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Affiliation(s)
- Ramya Mohan
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Arunmozhi Rama
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Ramalingam Karthik Raja
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Mohammed Rafi Shaik
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Mujeeb Khan
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Baji Shaik
- School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
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11
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Aziz MT, Mahmud SMH, Elahe MF, Jahan H, Rahman MH, Nandi D, Smirani LK, Ahmed K, Bui FM, Moni MA. A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron. Diagnostics (Basel) 2023; 13:2106. [PMID: 37371001 DOI: 10.3390/diagnostics13122106] [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: 05/03/2023] [Revised: 06/10/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.
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Affiliation(s)
- Md Tarek Aziz
- Centre for Advanced Machine Learning and Applications (CAMLAs), Bashundhara R/A, Dhaka 1229, Bangladesh
| | - S M Hasan Mahmud
- Centre for Advanced Machine Learning and Applications (CAMLAs), Bashundhara R/A, Dhaka 1229, Bangladesh
- Department of Computer Science, American International University-Bangladesh (AIUB), 408/1, Kuratoli, Khilkhet, Dhaka 1229, Bangladesh
| | - Md Fazla Elahe
- Centre for Advanced Machine Learning and Applications (CAMLAs), Bashundhara R/A, Dhaka 1229, Bangladesh
- Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Savar, Dhaka 1216, Bangladesh
| | - Hosney Jahan
- Centre for Advanced Machine Learning and Applications (CAMLAs), Bashundhara R/A, Dhaka 1229, Bangladesh
- Department of Computer Science & Engineering (CSE), Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka 1216, Bangladesh
| | - Md Habibur Rahman
- Centre for Advanced Machine Learning and Applications (CAMLAs), Bashundhara R/A, Dhaka 1229, Bangladesh
- Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh
| | - Dip Nandi
- Department of Computer Science, American International University-Bangladesh (AIUB), 408/1, Kuratoli, Khilkhet, Dhaka 1229, Bangladesh
| | - Lassaad K Smirani
- The Deanship of Information Technology and E-learning, Umm Al-Qura University, Mecca 24382, Saudi Arabia
| | - Kawsar Ahmed
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada
- Group of Biophotomatiχ, Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University (MBSTU), Tangail 1902, Bangladesh
| | - Francis M Bui
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada
| | - Mohammad Ali Moni
- Artificial Intelligence & Digital Health, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia
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12
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Nasir MU, Khan MF, Khan MA, Zubair M, Abbas S, Alharbi M, Akhtaruzzaman M. Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:1406545. [PMID: 37284488 PMCID: PMC10241593 DOI: 10.1155/2023/1406545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 06/08/2023]
Abstract
Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.
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Affiliation(s)
- Muhammad Umar Nasir
- Department of Computer Science, Bahria University, Lahore Campus, Lahore 54000, Pakistan
| | - Muhammad Farhan Khan
- Department of Forensic Sciences, University of Health Sciences, Lahore 54000, Pakistan
| | - Muhammad Adnan Khan
- Riphah School of Computing and Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan
- School of Information Technology, Skyline University College, University City Sharjah, Sharjah, UAE
| | - Muhammad Zubair
- Faculty of Computing, Riphah International University, Islamabad 45000, Pakistan
| | - Sagheer Abbas
- School of Computer Science, National College of Business Administration & Economics, Lahore 54000, Pakistan
| | - Meshal Alharbi
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharjb 11942, Saudi Arabia
| | - Md Akhtaruzzaman
- Department of Computer Science and Engineering, Aisan University of Bangladesh, Ashulia, Dhaka-1230, Bangladesh
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13
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Louis H, Mbim EN, Okon GA, Edet UO, Benjamin I, Ejiofor EU, Manicum ALE. Systematic exo-endo encapsulation of hydroxyurea (HU) by Cu, Ag, and Au-doped gallium nitride nanotubes (GaNNT) for smart therapeutic delivery. Comput Biol Med 2023; 161:106934. [PMID: 37257404 DOI: 10.1016/j.compbiomed.2023.106934] [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: 02/09/2023] [Revised: 04/07/2023] [Accepted: 04/13/2023] [Indexed: 06/02/2023]
Abstract
Similar to the more well-known carbon nanotubes, gallium nitride nanotubes (GaNNT) are among the materials that scientists have found to be extremely helpful in transporting drugs and to provide significant potential for multi-modal medical therapies. Here, the potential of Cu, Ag, and Au-doped GaNNT for smart delivery of the anticancer medication hydroxyurea (HU) was extensively investigated employing quantum chemical analysis and density functional theory (DFT) computation at the B3LYP-GD3BJ/def2-SVP level of theory. The systematic approach used in this study entails examining the exo (outside)-and endo (inside) loading of HU utilizing the investigated nanotubes in order to understand the adsorption, sensing processes, bonding types, and thermodynamic properties. Results of the HOMO-LUMO studies show that metal-doped GaNNTs with the hydroxyurea (HU) at the endo - interaction of the drug of the nanotube produced more reduced energy gaps (0.911-2.039 eV) compared with metal-doped GaNNTs complexes at the outside - interaction of the drug on the nanotube (2.25-3.22 eV) and as such reveal their suitability for use as drug delivery materials. As observed in the endo-interaction of HU adsorptions in the tubes, HU_endo_Au@GaNNT possessed the highest adsorption energy values of -118.716 kcal/mol which shows the most chemisorption between the surfaces and the adsorbate while for HU_exo_Ag@GaNNT is -97.431 kcal/mol for the highest exo-interactions. These results suggest that HU drug interacted inside the Ag, Au, and Cu doped GaNNT will be very proficient as a carrier of the HU drug into bio systems. These results are along with visual studies of weak interactions, thermodynamics, sensor, and drug release mechanisms suggest strongly the endo-encapsulation of HU as the best mode for smart drug delivery.
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Affiliation(s)
- Hitler Louis
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; Department of Pure and Applied Chemistry, University of Calabar, Calabar, Nigeria.
| | - Elizabeth N Mbim
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; Department of Public Health, Arthur Jarvis University, Akpabuyo, Nigeria
| | - Gideon A Okon
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; Department of Chemical Sciences, Clifford University, Owerrinta, Nigeria
| | - Uwem O Edet
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; Department of Microbiology, Arthur Jarvis University, Akpabuyo, Nigeria
| | - Innocent Benjamin
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; Department of Microbiology, Faculty of Biological Sciences, University of Calabar, Nigeria.
| | - Emmanuel U Ejiofor
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; Department of Chemical Sciences, Clifford University, Owerrinta, Nigeria
| | - Amanda-Lee E Manicum
- Department of Chemistry, Tshwane University of Technology, Pretoria, South Africa
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14
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Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment. Healthcare (Basel) 2022; 11:healthcare11010113. [PMID: 36611573 PMCID: PMC9818760 DOI: 10.3390/healthcare11010113] [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: 12/11/2022] [Revised: 12/25/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.
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15
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Wei Z, Liu X, Yan R, Sun G, Yu W, Liu Q, Guo Q. Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging. Front Genet 2022; 13:1002327. [PMID: 36386823 PMCID: PMC9644055 DOI: 10.3389/fgene.2022.1002327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/26/2022] [Indexed: 01/25/2023] Open
Abstract
Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer-Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer's global prediction and CNN's local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson's correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.
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Affiliation(s)
- Zhihao Wei
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Xi Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Ruiqing Yan
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Guocheng Sun
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China,School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Weiyong Yu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qiang Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China,School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing, China,*Correspondence: Qianjin Guo,
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16
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Nasir MU, Zubair M, Ghazal TM, Khan MF, Ahmad M, Rahman AU, Hamadi HA, Khan MA, Mansoor W. Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:7483. [PMID: 36236584 PMCID: PMC9572837 DOI: 10.3390/s22197483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/23/2022] [Accepted: 09/24/2022] [Indexed: 06/16/2023]
Abstract
Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.
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Affiliation(s)
- Muhammad Umar Nasir
- Riphah School of Computing and Innovation, Riphah International University Lahore Campus, Lahore 54000, Pakistan
| | - Muhammad Zubair
- Faculty of Computing, Riphah International University, Islamabad 45000, Pakistan
| | - Taher M. Ghazal
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia
- College of Computer and Information Technology, American University in the Emirates, Dubai Academic City, Dubai 503000, United Arab Emirates
| | - Muhammad Farhan Khan
- Department of Forensic Sciences, University of Health Sciences, Lahore 54000, Pakistan
| | - Munir Ahmad
- School of Computer Science, National College of Business Administration & Economics, Lahore 54000, Pakistan
| | - Atta-ur Rahman
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Hussam Al Hamadi
- College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
| | | | - Wathiq Mansoor
- College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
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17
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Nasir MU, Khan S, Mehmood S, Khan MA, Zubair M, Hwang SO. Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology. SENSORS (BASEL, SWITZERLAND) 2022; 22:6755. [PMID: 36146104 PMCID: PMC9500681 DOI: 10.3390/s22186755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/01/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes.
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Affiliation(s)
- Muhammad Umar Nasir
- Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan
| | - Safiullah Khan
- Department of IT Convergence Engineering, Gachon University, Seongnam 13120, Korea
| | - Shahid Mehmood
- Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan
| | - Muhammad Adnan Khan
- Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, Korea
| | - Muhammad Zubair
- Faculty of Computing, Riphah International University, Islamabad Campus, Islamabad 45000, Pakistan
| | - Seong Oun Hwang
- Department of Computer Engineering, Gachon University, Seongnam 13120, Korea
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18
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Ahmed MIB, Alotaibi S, Atta-ur-Rahman, Dash S, Nabil M, AlTurki AO. A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy. SN COMPUTER SCIENCE 2022; 3:437. [PMID: 35965953 PMCID: PMC9364307 DOI: 10.1007/s42979-022-01358-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/26/2022] [Indexed: 10/26/2022]
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19
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Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches. Diagnostics (Basel) 2022; 12:diagnostics12081899. [PMID: 36010249 PMCID: PMC9406837 DOI: 10.3390/diagnostics12081899] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.
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20
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Nasir MU, Khan S, Mehmood S, Khan MA, Rahman AU, Hwang SO. IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing. SENSORS (BASEL, SWITZERLAND) 2022; 22:5444. [PMID: 35891138 PMCID: PMC9325135 DOI: 10.3390/s22145444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.
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Affiliation(s)
- Muhammad Umar Nasir
- Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan; (M.U.N.); (S.M.)
| | - Safiullah Khan
- Department of IT Convergence Engineering, Gachon University, Seongnam 13120, Korea;
| | - Shahid Mehmood
- Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan; (M.U.N.); (S.M.)
| | - Muhammad Adnan Khan
- Pattern Recognition and Machine Learning Lab., Department of Software, Gachon University, Seongnam 13120, Korea
| | - Atta-ur Rahman
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia;
| | - Seong Oun Hwang
- Department of Computer Engineering, Gachon University, Seongnam 13120, Korea
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IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2650742. [PMID: 35909844 PMCID: PMC9334098 DOI: 10.1155/2022/2650742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 07/04/2022] [Indexed: 11/18/2022]
Abstract
A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, however, we focus on mitochondrial and multifactorial genetic disorders for prediction. Genetic illness is caused by a number of factors, including a defective maternal or paternal gene, excessive abortions, a lack of blood cells, and low white blood cell count. For premature or teenage life development, early detection of genetic diseases is crucial. Although it is difficult to forecast genetic disorders ahead of time, this prediction is very critical since a person's life progress depends on it. Machine learning algorithms are used to diagnose genetic disorders with high accuracy utilizing datasets collected and constructed from a large number of patient medical reports. A lot of studies have been conducted recently employing genome sequencing for illness detection, but fewer studies have been presented using patient medical history. The accuracy of existing studies that use a patient's history is restricted. The internet of medical things (IoMT) based proposed model for genetic disease prediction in this article uses two separate machine learning algorithms: support vector machine (SVM) and K-Nearest Neighbor (KNN). Experimental results show that SVM has outperformed the KNN and existing prediction methods in terms of accuracy. SVM achieved an accuracy of 94.99% and 86.6% for training and testing, respectively.
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