Features Selection for Facial Expression Recognition

Ewees, Ahmed A.; ELLaban, Hend Ab.; ElEraky, Rania M.

doi:10.1109/icccnt45670.2019.8944459

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…Their study underscored the importance of preprocessing techniques in improving classification accuracy for dermatoscopic images. In addition to these advancements several methods have been applied to medical image segmentation [18,19]. Eltayef et al [20] proposed an automated approach for melanoma border detection based on PSO and Markov Random Field (MRF) algorithms, achieving accurate edge detection.…”

Section: Previous Workmentioning

confidence: 99%

Image Segmentation Using 2D Discrete Wavelet Transform for Medical Image

Jarrar

2024

Preprint

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Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring. In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform. Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall. Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods. By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.

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Section: Previous Workmentioning

confidence: 99%

Image Segmentation Using 2D Discrete Wavelet Transform for Medical Image

Jarrar

2024

Preprint

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“…In general, the feature selection methods are classified into two techniques: wrapper and filter methods. The wrapper methods depend on the classification algorithm to evaluate the selected features subset; however, the accuracy of these methods is influenced by the attributes of the used classifier; therefore, if the parameters of the classifier are not accurately chosen, this may lead to a degradation of the accuracy of the selected features [19] [20]. Unlike the wrapper methods, the filter methods do not depend on the classification algorithm to evaluate the selected features and they are also less expensive than the wrapper methods.…”

Section: Introductionmentioning

confidence: 99%