Deep Learning for Ovarian Tumor Classification with Ultrasound Images

Wu, Cheng‐Zhu; Wang, Yamei; Wang, Feng

doi:10.1007/978-3-030-00764-5_36

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…DCNN solutions appear to offer high levels of accuracy in classification, but their decisions are made in a black-box manner with no explanation. Nevertheless, we still plan to conduct such an evaluation on recent works reported in the literature such as that in Wu et al 22 when a sufficient number of US scan images of ovarian masses become available.…”

Section: Discussionmentioning

confidence: 99%

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An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Al-karawi¹,

Al-Assam

et al. 2021

Ultrason Imaging

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Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

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Section: Discussionmentioning

confidence: 99%

“…21 More recently, deep convolutional neural networks (DCNN) have been extensively applied. 22 Such a DCNN learns useful image features at various scales through layers of linear convolutions, non-linear transforms, and down-sampling.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Al-karawi¹,

Al-Assam

et al. 2021

Ultrason Imaging

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“…Secondly, comparison of different CNN models performed on the augmented data set of normal, benign and malignant. CNN models include GooglENet_V3, DenSeNET, ResNet-50 and VGG_16 in which GooglENet_V3 achieve outclassed accuracy of 92.50% [54].…”

Section: Classification Of Ovarian Cancer With Deep Learningmentioning

confidence: 99%

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Sajjad

Khan

Nawaz

et al. 2022

JCBI

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Machine and Deep learning has witnessed an exceptional amount of admiration in recent years. ML has ability to learn data itself by predicting uncertain conditions or future and classify categories with minimum intervention of human. While in DL, computers are able to automatically, learn useful features and representation precisely from raw data. ML and DL potentially a disruptive technology in predictive healthcare analysis. A detailed understanding and evaluation of the applications and principles of radiomics, machine and deep learning is an important task to construct possible solutions that are capable of accomplish compulsory and ethical requirements, which can enhance efficiency, quality and outcomes. Machine and deep learning extensively being use in medical image analysis, medical diagnostics and medical image technology. The wide scope and sudden progress of ML and DL has remarkably change the ways of diagnosis, prediction, classification and analyzing ovarian, lungs, brain, skin and various other types of cancer. In view of multiple applications of ML and DL, in this article a review conducted on ovarian cancer. This review comprises the detailed analysis of OC (prognosis, diagnosis, classification, evaluation) by covering all the major contributions of machine and deep learning. Furthermore, a literature taxonomy of the research conferred and emerging aspects analyzed. A section of discussion also signified to elaborate the limitations and future challenges of machine and deep learning comprises with ovarian cancer.

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“…To begin by noticing an ultrasound image dataset containing 988 ovarian cyst image samples of three ovarian tumour forms, despite the lack of publicly available ultrasounds images. Secondly, compare the overall CNN models' overall capacity [12]. Many studies have used Artificial neural networks (ANN) to resolve the classification of ovarian cancer.…”

Section: Related Work On Ovarian Classificationmentioning

confidence: 99%

Ovarian Cysts Classification Using Novel Deep Q-Learning With Harris Hawks Optimization Method

Narmatha

Manimegalai

Krishnadass³

et al. 2021

Preprint

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This research presents an essential solution for classifying ultrasound diagnostic images describing seven types of ovarian cysts: Follicular cyst, Hemorrhagic cyst, Corpus luteum cyst, Polycystic-appearing ovary, endometriosis cysts, Dermoid cyst, and Teratoma. This work proposed a novel technique using images of ovarian ultrasound cysts from an ongoing database with this motivation. Initially, the work is followed by removing noise in preprocessing, feature extraction, and finally classifying using new Deep Q-Network with Harris Hawks Optimization (HHO) classifier. Automatic feature extraction is implemented using the recent popular convolutional neural network (CNN) technique that extracts image features as conditions in the reinforcement learning algorithm. With this, through the procedure of a new deep Q-learning algorithm, Deep Q-Network (DQN) is generated to train a Q-network. The swarm-based method of HHO utilized the optimization method to produce optimal hyperparameters in the DQN model known as HHO-DQN, a novel technique for classifying ovarian cysts. Extensive experimental evaluations on datasets show that the proposed HHO- DQN approach outperforms existing active learning approaches for ovarian cyst classification. Compared with the ANN, CNN, and AlexNet models, the performance of the proposed model is better in terms of precision, f-measure, recall, accuracy, and IoU. The proposed model has achieved 96% precision, 96.5% f-measure, 96% recall, 97% accuracy, and 0.65 IoU.

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Deep Learning for Ovarian Tumor Classification with Ultrasound Images

Cited by 14 publications

References 16 publications

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Ovarian Cysts Classification Using Novel Deep Q-Learning With Harris Hawks Optimization Method

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