A Convolutional Neural Network Approach for Detecting Malignancy of Ovarian Cancer

Mathur, Mansi; Jindal, Vikas

doi:10.1007/978-981-16-1249-7_29

Cited by 1 publication

(1 citation statement)

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“…Deep learning provides the advantage of learning discriminative features, which are implemented through repeated learning and correction to identify hidden rules and patterns in images, thereby improving the accuracy of classification. In recent years, some studies have also proposed the use of deep learning technology to diagnose ovarian tumors from various medical images [18,19], including magnetic resonance imaging [20,21], computed tomography [22], histopathological image [23], and ultrasound [24][25][26][27]. Ultrasonography is the most basic and commonly used way to distinguish benign and malignant ovarian tumors in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging

Hsu

Hung

et al. 2022

BMC Med Inform Decis Mak

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Background Upon the discovery of ovarian cysts, obstetricians, gynecologists, and ultrasound examiners must address the common clinical challenge of distinguishing between benign and malignant ovarian tumors. Numerous types of ovarian tumors exist, many of which exhibit similar characteristics that increase the ambiguity in clinical diagnosis. Using deep learning technology, we aimed to develop a method that rapidly and accurately assists the different diagnosis of ovarian tumors in ultrasound images. Methods Based on deep learning method, we used ten well-known convolutional neural network models (e.g., Alexnet, GoogleNet, and ResNet) for training of transfer learning. To ensure method stability and robustness, we repeated the random sampling of the training and validation data ten times. The mean of the ten test results was set as the final assessment data. After the training process was completed, the three models with the highest ratio of calculation accuracy to time required for classification were used for ensemble learning pertaining. Finally, the interpretation results of the ensemble classifier were used as the final results. We also applied ensemble gradient-weighted class activation mapping (Grad-CAM) technology to visualize the decision-making results of the models. Results The highest mean accuracy, mean sensitivity, and mean specificity of ten single CNN models were 90.51 ± 4.36%, 89.77 ± 4.16%, and 92.00 ± 5.95%, respectively. The mean accuracy, mean sensitivity, and mean specificity of the ensemble classifier method were 92.15 ± 2.84%, 91.37 ± 3.60%, and 92.92 ± 4.00%, respectively. The performance of the ensemble classifier is better than that of a single classifier in three evaluation metrics. Moreover, the standard deviation is also better which means the ensemble classifier is more stable and robust. Conclusion From the comprehensive perspective of data quantity, data diversity, robustness of validation strategy, and overall accuracy, the proposed method outperformed the methods used in previous studies. In future studies, we will continue to increase the number of authenticated images and apply our proposed method in clinical settings to increase its robustness and reliability.

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