Karthick Natarajan and Kamatchi Annappan (2023) Enhancing Endometrial Cancer Detection by Feature Entanglement Image Generator and Multimodal Feature Learning with Attention Mechanism. Enhancing Endometrial Cancer Detection by Feature Entanglement Image Generator and Multimodal Feature Learning with Attention Mechanism, 16 (5). pp. 238-250. ISSN 2185-3118

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Abstract

Endometrial cancer (EC) rate is rising progressively worldwide so early diagnosis of EC using medical
imaging is vital to increase a patient’s survival rate. To avoid the misdiagnosis of EC on magnetic resonance imaging
(MRI) scans by clinicians, an automated staging model based on deep learning has broadly emerged in medical systems.
Many deep learning models like convolutional neural networks (CNNs) with transfer learning schemes are developed
to classify normal and cancer patients from a sequence of MRI scans. But such models have poor sensitivity to classify
EC stages from other benign lesions, resulting inaccurate diagnoses of EC. Also, the classical transfer learning schemes
reduce the accuracy of detecting medical images due to the discrepancies in data distribution between the source and
target domains. Hence, this article proposes a novel deep EC prediction (DeepECP) model that involves image
synthesis and classification processes. First, a feature entanglement generative adversarial network (FE-GAN) is
proposed for MRI synthesis that creates a desired MRI sequence according to the complementary features of multiple
MRI modalities. Then, a multi-modal CNN with long short-term memory (LSTM) network followed by the fully
connected (FC) layer is developed to extract a sequence of cancer features from multi-modal MRI sequences. Moreover,
an attention strategy is used to fuse those extracted features and get a final feature vector, which is given to the softmax
function to classify EC stages. Finally, the extensive experiments show that the DeepECP model on the TCGA-UCEC
and CPTAC-UCEC datasets reaches 93.2% and 93.3% accuracy in detecting EC stages, respectively compared to the
support vector machine (SVM), VGGNet-16, InceptionResNet and CNN models.

Item Type: Article
Uncontrolled Keywords: Endometrial cancer, Medical imaging, Automated staging, Deep learning, GAN, CNN-LSTM.
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 04 Sep 2024 04:49
Last Modified: 04 Sep 2024 04:49
URI: https://ir.psgcas.ac.in/id/eprint/2204

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