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Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques.
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- Author(s): Ahmad, Mehran; Irfan, Muhammad Abeer; Sadique, Umar; Haq, Ihtisham ul; Jan, Atif; Khattak, Muhammad Irfan; Ghadi, Yazeed Yasin; Aljuaid, Hanan
- Source:
Cancers; Nov2023, Vol. 15 Issue 21, p5247, 31p- Subject Terms:
HEAD & neck cancer diagnosis; DEEP learning; COMPUTERS in medicine; BIOPSY; MATHEMATICAL models; HEAD & neck cancer; ARTIFICIAL intelligence; COST control; DIAGNOSTIC imaging; DECISION support systems; THEORY; FACTOR analysis; DESCRIPTIVE statistics; EMPLOYEES' workload; RESEARCH funding; RECEIVER operating characteristic curves; SENSITIVITY & specificity (Statistics); SQUAMOUS cell carcinoma; EARLY diagnosis; ALGORITHMS - Source:
- Additional Information
- Abstract: Simple Summary: The research aimed to address the challenges in the early diagnosis of Oral Squamous Cell Carcinoma (OSCC), a critical concern given its high fatality rate and global prevalence. Through the development of hybrid methodologies, the study sought to improve early diagnosis, reduce the burden on pathologists, and enhance the accuracy of OSCC diagnosis. By employing transfer learning, a combination of CNN and SVMs, and a fusion of deep and texture-based features, the research achieved a significant overall accuracy of 97.00%, effectively addressing the critical problem of timely and accurate OSCC diagnosis. 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. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Cancers is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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