Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer
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Published:2023-07-31
Issue:15
Volume:13
Page:2538
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ISSN:2075-4418
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Container-title:Diagnostics
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language:en
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Short-container-title:Diagnostics
Author:
Hamdi Mohammed1ORCID, Senan Ebrahim Mohammed2ORCID, Awaji Bakri1, Olayah Fekry3, Jadhav Mukti E.4ORCID, Alalayah Khaled M.5ORCID
Affiliation:
1. Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia 2. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, Yemen 3. Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia 4. Shri Shivaji Science & Arts College, Chikhli Dist., Buldana 443112, India 5. Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
Abstract
Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best and most commonly used for cervical cancer screening and are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis. Manual diagnosis by microscopes is limited and prone to manual errors, and tracking all cells is difficult. Therefore, the development of computational techniques is important as diagnosing many samples can be done automatically, quickly, and efficiently, which is beneficial for medical laboratories and medical professionals. This study aims to develop automated WSI image analysis models for early diagnosis of a cervical squamous cell dataset. Several systems have been designed to analyze WSI images and accurately distinguish cervical cancer progression. For all proposed systems, the WSI images were optimized to show the contrast of edges of the low-contrast cells. Then, the cells to be analyzed were segmented and isolated from the rest of the image using the Active Contour Algorithm (ACA). WSI images were diagnosed by a hybrid method between deep learning (ResNet50, VGG19 and GoogLeNet), Random Forest (RF), and Support Vector Machine (SVM) algorithms based on the ACA algorithm. Another hybrid method for diagnosing WSI images by RF and SVM algorithms is based on fused features of deep-learning (DL) models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet). It is concluded from the systems’ performance that the DL models’ combined features help significantly improve the performance of the RF and SVM networks. The novelty of this research is the hybrid method that combines the features extracted from deep-learning models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet) with RF and SVM algorithms for diagnosing WSI images. The results demonstrate that the combined features from deep-learning models significantly improve the performance of RF and SVM. The RF network with fused features of ResNet50-VGG19 achieved an AUC of 98.75%, a sensitivity of 97.4%, an accuracy of 99%, a precision of 99.6%, and a specificity of 99.2%.
Funder
Najran University, Kingdom of Saudi Arabia
Subject
Clinical Biochemistry
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