Ultrasound-Based Ovarian Cysts Detection with Improved Machine-Learning Techniques and Stage Classification Using Enhanced Classifiers

Athithan, Senthil; Sachi, Savya; Singh, Ajay Kumar

doi:10.1007/s42979-023-01973-0

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…The effectiveness of the EGNNN-NPOA-PM-UI method is evaluated under performance metrics. The obtained results are analyzed with existing models, like ultrasound image examination utilizing DNN for differentiating between benign and malignant ovarian tumors (DNN-VGG16-ResNet50-MobileNet-PM-UI) [19], automatic ovarian tumor identification scheme depending on ensemble convolutional neural network, and ultrasound imaging (CNN-Grad-CAM-PM-UI) [20], adoption of radiomics and machine learning upgrades for the diagnostic processes of women with ovarian masses (SVM-PM-UI) [21], machine learning method used with gynecological ultrasound to forecast progression-free survival in ovarian tumor patients (LR-RFF-KNN-PM-UI) [22], a deep learning method for ovarian cyst identification and categorization (OCD-FCNN) using fuzzy convolutional neural network (FCNN-PM-UI) [23], ovarian cancer estimation from ovarian cysts depending upon TVUS under machine learning strategies (RF-KNN-XGBoost-PM-UI) [24], ultrasound-based ovarian cyst detection with improved machine-lLearning strategies and stage classification under enhanced classifiers (ANN-DC-SVM-PM-UI) [25] and ovarian cyst categorization utilizing deep reinforcement learning and Harris Hawks optimization (DQN-HHOA-PM-UI) [26], respectively. K-fold cross-validation is considered.…”

Section: Resultsmentioning

confidence: 99%

“…Tables 3-8 and Figure 4 depict the simulation results of the proposed EGNNN-NPOA-PM-UI method. Then the proposed EGNNN-NPOA-PM-UI method is likened with existing systems, namely, DNN-VGG16-ResNet50-MobileNet-PM-UI [19]; CNN-Grad-CAM-PM-UI [20]; SVM-PM-UI [21]; LR-RFF-KNN-PM-UI [22]; FCNN-PM-UI [23]; RF-KNN-XGBoost-PM-UI [24]; ANN-DC-SVM-PM-UI [25] and DQN-HHOA-PM-UI [26], respectively. Table 5 depicts the specificity analysis.…”

Section: Performance Analysismentioning

confidence: 99%

“…Athithan et al [25] presented ultrasound-based ovarian cyst identification with improved machine-learning and stage classification depending on enhanced classifiers. Artificial neural networks, discriminant classifiers, and support vector machines were utilized.…”

Section: Introductionmentioning

confidence: 99%

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Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer

Deeparani,

Kalamani

2023

Diagnostics

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Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI). The real time ultrasound pelvic mass images are augmented using random transformation. Then the augmented images are given to the 3D Tsallis entropy-based multilevel thresholding technique for extraction of the ROI region and its features are further extracted with the help of fast discrete curvelet transform with the wrapping (FDCT-WRP) method. Therefore, in this work, EGNNN optimized with nomadic people optimizer (NPOA) was utilized for classifying the gynecological abdominal pelvic masses. It was executed in PYTHON and the efficiency of the proposed method analyzed under several performance metrics. The proposed EGNNN-NPOA-PM-UI methods attained 99.8%. Ultrasound image analysis using the proposed EGNNN-NPOA-PM-UI methods can accurately predict pelvic masses analyzed with the existing methods.

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