TLOD: Innovative ovarian tumor detection for accurate multiclass classification and clinical application

Sundari, M. Jeya; Brintha, N. C.

doi:10.1007/s13721-024-00454-5

Netw Model Anal Health Inform Bioinforma

2024

DOI: 10.1007/s13721-024-00454-5

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TLOD: Innovative ovarian tumor detection for accurate multiclass classification and clinical application

M. Jeya Sundari,

N. C. Brintha

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2024

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Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model

Sha

2024

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Ovarian cysts pose significant health risks including torsion, infertility, and cancer, necessitating rapid and accurate diagnosis. Ultrasonography is commonly employed for screening, yet its effectiveness is hindered by challenges like weak contrast, speckle noise, and hazy boundaries in images. This study proposes an adaptive deep learning-based segmentation technique using a database of ovarian ultrasound cyst images. A Guided Trilateral Filter (GTF) is applied for noise reduction in pre-processing. Segmentation utilizes an Adaptive Convolutional Neural Network (AdaResU-net) for precise cyst size identification and benign/malignant classification, optimized via the Wild Horse Optimization (WHO) algorithm. Objective functions Dice Loss Coefficient and Weighted Cross-Entropy are optimized to enhance segmentation accuracy. Classification of cyst types is performed using a Pyramidal Dilated Convolutional (PDC) network. The method achieves a segmentation accuracy of 98.87%, surpassing existing techniques, thereby promising improved diagnostic accuracy and patient care outcomes.

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Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model

Sha

2024

Sci Rep

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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TLOD: Innovative ovarian tumor detection for accurate multiclass classification and clinical application

Cited by 1 publication

References 23 publications

Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model

Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model

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