A hybrid deep neural network‐based automated diagnosis system using x‐ray images and clinical findings

Aydoğan, Murat

doi:10.1002/ima.22856

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…The integration of audio and written content has improved, enhancing user comprehension and experience. Consequently, handling text without this combination has become more cumbersome and complex, underscoring the importance of bimodal content [2]. Challenges persist in communication via speech, including variability in the precision of emotional speech detection and a shortage of foundational understanding centered on emotions.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Emotion Recognition Using Bi-LG-GCN for MELD Dataset

Alsaadawı,

Daş

2024

Balkan Journal of Electrical and Computer Engineering

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Emotion recognition using multimodal data is a widely adopted approach due to its potential to enhance human interactions and various applications. By leveraging multimodal data for emotion recognition, the quality of human interactions can be significantly improved. We present the Multimodal Emotion Lines Dataset (MELD) and a novel method for multimodal emotion recognition using a bi-lateral gradient graph neural network (Bi-LG-GNN) and feature extraction and pre-processing. The multimodal dataset uses fine-grained emotion labeling for textual, audio, and visual modalities. This work aims to identify affective computing states successfully concealed in the textual and audio data for emotion recognition and sentiment analysis. We use pre-processing techniques to improve the quality and consistency of the data to increase the dataset’s usefulness. The process also includes noise removal, normalization, and linguistic processing to deal with linguistic variances and background noise in the discourse. The Kernel Principal Component Analysis (K-PCA) is employed for feature extraction, aiming to derive valuable attributes from each modality and encode labels for array values. We propose a Bi-LG-GCN-based architecture explicitly tailored for multimodal emotion recognition, effectively fusing data from various modalities. The Bi-LG-GCN system takes each modality's feature-extracted and pre-processed representation as input to the generator network, generating realistic synthetic data samples that capture multimodal relationships. These generated synthetic data samples, reflecting multimodal relationships, serve as inputs to the discriminator network, which has been trained to distinguish genuine from synthetic data. With this approach, the model can learn discriminative features for emotion recognition and make accurate predictions regarding subsequent emotional states. Our method was evaluated on the MELD dataset, yielding notable results in terms of accuracy (80%), F1-score (81%), precision (81%), and recall (81%) when using the MELD dataset. The pre-processing and feature extraction steps enhance input representation quality and discrimination. Our Bi-LG-GCN-based approach, featuring multimodal data synthesis, outperforms contemporary techniques, thus demonstrating its practical utility.

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Section: Introductionmentioning

confidence: 99%

Multimodal Emotion Recognition Using Bi-LG-GCN for MELD Dataset

Alsaadawı,

Daş

2024

Balkan Journal of Electrical and Computer Engineering

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“…Deep neural networks (DNN) [1][2][3] have achieved excellent results in a variety of computer vision tasks. 4 However, models trained on the training data (source domain) cannot perform well on the testing data (target domain) when the data are drawn from different distributions.…”

Section: Introductionmentioning

confidence: 99%

Fusing feature and output space for unsupervised domain adaptation on medical image segmentation

Wang

et al. 2023

Int J Imaging Syst Tech

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Image segmentation requires large amounts of annotated data. However, collecting massive datasets with annotations is difficult since they are expensive and labor‐intensive. The unsupervised domain adaptation (UDA) for image segmentation is a promising approach to address the label‐scare problem on the target domain, which enables the trained model on the source labeled domain to be adaptive to the target domain. The adversarial‐based methods encourage extracting the domain‐invariant features by training a domain discriminator to mitigate the domain gap. Existing UDA segmentation methods fail to obtain satisfied segmentation results as they only consider the global knowledge of output space while neglecting the local information of feature space. In this paper, a fusing feature and output (FFO) space method is proposed for UDA, which in the context of medical image segmentation. The proposed model is learned by training a more powerful domain discriminator, which considers features extracted from both feature space and output space. Extensive experiments carried out on several medical image datasets show the adaptation effectiveness of our approach in improving the segmentation performance.

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A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images

Maheswari,

Gopalakrishnan

2024

Int. j. inf. tecnol.

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A hybrid deep neural network‐based automated diagnosis system using x‐ray images and clinical findings

Cited by 3 publications

References 55 publications

Multimodal Emotion Recognition Using Bi-LG-GCN for MELD Dataset

Multimodal Emotion Recognition Using Bi-LG-GCN for MELD Dataset

Fusing feature and output space for unsupervised domain adaptation on medical image segmentation

A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images

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