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

Ovarian Cysts Classification Using Novel Deep Q-Learning With Harris Hawks Optimization Method | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ovarian Cysts Classification Using Novel Deep Q-Learning With Harris Hawks Optimization Method Narmatha C, Manimegalai P, Krishnadass J, Prajoona Valsalan, Manimurugan S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-920250/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract 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. Computer Architecture and Engineering Electrical Engineering Active learning Deep Q-learning deep learning image classification Ultrasound Medical Image Ovarian Cyst Classification Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Sep, 2021 Reviewers invited by journal 24 Sep, 2021 First submitted to journal 18 Sep, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-920250","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":54270536,"identity":"ab3dd875-f0ee-4d49-8fb1-8a32ff5d0943","order_by":0,"name":"Narmatha 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