An Intelligent Black Widow Optimization on Image Enhancement with Deep Learning Based Ovarian Tumor Diagnosis model

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

doi:10.1080/21681163.2022.2092036

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Most DL approaches have not been utilized for real-time applications in ovarian cancer diagnosis. However, in the future, these approaches have the potential to be employed for real-time diagnostic purposes in ovarian cancer [ 73 ].…”

Section: Resultsmentioning

confidence: 99%

“…In another study, Sundari and Brintha designed image enhancement with a deep learning-based ovarian tumour diagnosis (IEDL-OVD) method, which was due to this improved image quality, then improve optimization through the black widow optimization algorithm (BWOA) [ 77 ], and used some feature extraction and classification techniques to maximize the precision and recall rate. The IEDL-OVD model has obtained an increased precision and recall of 73.50% and 61.20%, respectively [ 73 ].…”

Section: Resultsmentioning

confidence: 99%

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Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Sadeghi,

Sina,

Omidi

et al. 2024

Pol J Radiol

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Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. <br /> This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies.<br /> The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Sadeghi,

Sina,

Omidi

et al. 2024

Pol J Radiol

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show abstract

“…The CA-125 blood test is done to check the protein level in the blood. 7 Numerous machine learning (ML) methods 8 such as Decision Trees, Artificial Neural Networks, Support Vector Machines, and Bayesian Networks are also used in cancer research to increase the effectiveness and accuracy. 9 When compared to radionics and radiologists, the Deep Learning models can be used to separate malignant and benign ovarian tumors noninvasively with excellent specificity and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Factorization‐based active contour segmentation and pelican optimization‐based modified bidirectional long short‐term memory for ovarian tumor detection

Sundari

Brintha

2022

Int J Imaging Syst Tech

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This paper proposes a novel three-dimensional convolution neural networkbased modified bidirectional long short-term memory with pelican optimization (3D CNN based MBiLSTM with PO) algorithm for multiclass ovarian tumor detection. Initially, the International Collaboration on Cancer Reporting endometrial cancer dataset images are provided in pre-processing phase, which uses a pre-emphasis filter to process the input image. In the segmentation phase, pre-processed data is then partitioned into diverse subgroups (i.e., pixels), which minimizes the complexity of images. In this paper, a factorization-based active contour technique is employed in the effective segmentation of images. The segmented features are then extracted and classified using the 3D CNN-MBiLSTM with PO algorithm. Finally, the experimental results are conducted and compared with various other approaches for various performance metrics. Each metric is evaluated with respect to the different number of iterations. The accuracy, sensitivity, and specificity have reached a higher value of 98.5%, 96%, and 98.25%, respectively.

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Intelligent Fault Diagnosis System of Electrical Equipment Based on Neural Network Algorithm

Zhang,

Zhang

2024

Procedia Computer Science

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An Intelligent Black Widow Optimization on Image Enhancement with Deep Learning Based Ovarian Tumor Diagnosis model

Cited by 3 publications

References 21 publications

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Factorization‐based active contour segmentation and pelican optimization‐based modified bidirectional long short‐term memory for ovarian tumor detection

Intelligent Fault Diagnosis System of Electrical Equipment Based on Neural Network Algorithm

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