Towards Building Robust Song and Location Detection System Using LBP Features

Umamaheswari, S.; Priya, K.Hari; Allinjoe, D.

doi:10.1109/icaeca52838.2021.9675716

Cited by 2 publications

(1 citation statement)

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“…One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. To enhance accuracy, the earlier detection and, for diverse perspectives, the proposed research analyzed the techniques used in [19], [21], and [23] to apply in medical imaging analysis, including the classification of Pap smear images for cervical cancer detection. A nominated texture-based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes was presented in [20].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Cervical Cancer Classification with SVM and Improved Genetic Algorithm on Pap Smear Images

Umamaheswari,

Birnica,

Boobalan

et al. 2024

CSJM

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This study presents an approach to optimize cervical cancer classification using Support Vector Machines (SVM) and an improved Genetic Algorithm (GA) on Pap smear images. The proposed methodology involves preprocessing the images, extracting relevant features, and employing a genetic algorithm for feature selection. An SVM classifier is trained using the selected features and optimized using the genetic algorithm. The performance of the optimized model is evaluated, demonstrating improved accuracy and efficiency in cervical cancer classification. The findings hold the potential for assisting healthcare professionals in early cervical cancer diagnosis based on Pap smear images.

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Section: Related Workmentioning

confidence: 99%