aspg-18-4118-1792480807
20261020072007
American Scientific Publishing Group
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American Scientific Publishing Group
Journal of Intelligent Systems and Internet of Things
JISIoT
2769-786X
2690-6791
2025
17
2
Multi-View Feature Learning Approach in Deep Learning Model for Improving Endometrial Cancer Detection from Medical Images
Karthick
Natarajan
Research Scholar, Department of Computer Science, PSG College of Arts and Science, Coimbatore- 641014, Tamilnadu, India
Nithya
Palanisamy
Associate Professor and Head , Department of Networking and Mobile Application, PSG College of Arts and Science, Coimbatore- 641014, Tamilnadu, India
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An accurate diagnosis of Endometrial Cancer (EC) is crucial for gynecologists, as different types may require specific treatments. Radiomics, a quantitative method, can help analyze and quantify image heterogeneity, aiding in lesion diagnosis. Previous research introduced a Transformer-based Semantic-Aware U-Net with Deep Endometrial Cancer Prediction (TSA-UNet-DeepECP) to segment and classify EC stages in Magnetic Resonance Imaging MRI scans. However, the heterogeneous properties of input scans can affect the DeepECP model's performance. Hence, this study presents the TSA-UNet with an Improved DeepECP model (TSA-UNet-IDeepECP) for EC stage classification. This IDeepECP model incorporates a multi-view learning approach, combining local 2D MRI image information with global 3D MRI image information. First, the endometrium MRI scans are collected, augmented, and segmented using the TSA-UNet model. Various Deep Learning (DL) models, one for 2D and one for 3D, are fed the segmented images. In contrast to the 3D view model, which collects global information from 3D MRI images, the 2D view model primarily recovers local features from 2D MRI data. The multi-view DeepECP model is trained using these combined characteristics. A Fully Connected (FC) layer and the softmax classifier are used for classifying EC stages using the combined features. When compared to traditional models, a TSA-UNet-IDeepECP model achieves better performance in EC detection from MRI images.
2025
238
249
4118
https://creativecommons.org/licenses/by/4.0/
10.54216/JISIoT.170215
https://www.americaspg.com/journal/18/article/4118
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https://www.americaspg.com/storage/31758301249.pdf