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Hossain T, Shamrat FMJM, Zhou X, Mahmud I, Mazumder MSA, Sharmin S, Gururajan R. Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis. PeerJ Comput Sci 2024; 10:e1950. [PMID: 38660192 PMCID: PMC11041948 DOI: 10.7717/peerj-cs.1950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/29/2024] [Indexed: 04/26/2024]
Abstract
Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model's internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model's proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide.
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Affiliation(s)
- Tanzim Hossain
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | | | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, Australia
| | - Imran Mahmud
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Md. Sakib Ali Mazumder
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Sharmin Sharmin
- Department of Computer System and Technology, University of Malaya, Kuala Lumpur, Malaysia
| | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, Australia
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Sutradhar A, Al Rafi M, Shamrat FMJM, Ghosh P, Das S, Islam MA, Ahmed K, Zhou X, Azad AKM, Alyami SA, Moni MA. BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients. Sci Rep 2023; 13:22874. [PMID: 38129433 PMCID: PMC10739972 DOI: 10.1038/s41598-023-48486-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
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Affiliation(s)
- Ananda Sutradhar
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Mustahsin Al Rafi
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - F M Javed Mehedi Shamrat
- Department of Computer System and Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Pronab Ghosh
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Subrata Das
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Md Anaytul Islam
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Kawsar Ahmed
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - A K M Azad
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Saudi Arabia
| | - Salem A Alyami
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Saudi Arabia
| | - Mohammad Ali Moni
- Centre for AI & Digital Health Technology, Artificial Intelligence & Cyber Future Institute, Charles Stuart University, Bathurst, NSW, 2795, Australia.
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Ghosh P, Azam S, Quadir R, Karim A, Shamrat FMJM, Bhowmik SK, Jonkman M, Hasib KM, Ahmed K. SkinNet-16: A deep learning approach to identify benign and malignant skin lesions. Front Oncol 2022; 12:931141. [PMID: 36003775 PMCID: PMC9395205 DOI: 10.3389/fonc.2022.931141] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/07/2022] [Indexed: 12/24/2022] Open
Abstract
Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method. Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the region of interest (ROI), region-based segmentation, morphological gradient, and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A principle component analysis (PCA)-based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network-based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.
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Affiliation(s)
- Pronab Ghosh
- Department of Computer Science (CS), Lakehead University, Thunder Bay, ON, Canada
| | - Sami Azam
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
- *Correspondence: Sami Azam,
| | - Ryana Quadir
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Asif Karim
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
| | - F. M. Javed Mehedi Shamrat
- Department of Computer Science and Engineering, Ahsanullah University of Science & Technology, Dhaka, Bangladesh
| | - Shohag Kumar Bhowmik
- Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Mirjam Jonkman
- College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
| | - Khan Md. Hasib
- Department of Computer Science and Engineering, Ahsanullah University of Science & Technology, Dhaka, Bangladesh
| | - Kawsar Ahmed
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
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Shamrat FMJM, Azam S, Karim A, Islam R, Tasnim Z, Ghosh P, De Boer F. LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images. J Pers Med 2022; 12:jpm12050680. [PMID: 35629103 PMCID: PMC9143659 DOI: 10.3390/jpm12050680] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/01/2022] [Accepted: 04/20/2022] [Indexed: 12/29/2022] Open
Abstract
In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed. The classification is conducted on 10 disease classes of the lungs, namely COVID-19, Effusion, Tuberculosis, Pneumonia, Lung Opacity, Mass, Nodule, Pneumothorax, and Pulmonary Fibrosis, along with the Normal class. The dataset is a collective dataset gathered from multiple sources. After pre-processing and balancing the dataset with eight augmentation techniques, a total of 80,000 X-ray images were fed to the model for classification purposes. Initially, eight pre-trained CNN models, AlexNet, GoogLeNet, InceptionV3, MobileNetV2, VGG16, ResNet 50, DenseNet121, and EfficientNetB7, were employed on the dataset. Among these, the VGG16 achieved the highest accuracy at 92.95%. To further improve the classification accuracy, LungNet22 was constructed upon the primary structure of the VGG16 model. An ablation study was used in the work to determine the different hyper-parameters. Using the Adam Optimizer, the proposed model achieved a commendable accuracy of 98.89%. To verify the performance of the model, several performance matrices, including the ROC curve and the AUC values, were computed as well.
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Affiliation(s)
- F. M. Javed Mehedi Shamrat
- Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (F.M.J.M.S.); (Z.T.)
| | - Sami Azam
- College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT 0909, Australia; (A.K.); (F.D.B.)
- Correspondence:
| | - Asif Karim
- College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT 0909, Australia; (A.K.); (F.D.B.)
| | - Rakibul Islam
- Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh;
| | - Zarrin Tasnim
- Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (F.M.J.M.S.); (Z.T.)
| | - Pronab Ghosh
- Department of Computer Science (CS), Lakehead University, 955 Oliver Rd, Thunder Bay, ON P7B 5E1, Canada;
| | - Friso De Boer
- College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT 0909, Australia; (A.K.); (F.D.B.)
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