Mutation based Atom Search Optimization Algorithm for Hyperparameter Optimization of the DenseNet121 Architecture for Cervical Cancer Classification
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The provided text is a metadata record for a preprint titled "Mutation based Atom Search Optimization Algorithm for Hyperparameter Optimization of the DenseNet121 Architecture for Cervical Cancer Classification" that has been retracted and removed from Research Square. The authors requested the removal of this work, which focused on applying a mutation-based optimization algorithm to tune a deep learning architecture for classifying cervical cancer. No scientific findings or methodological details are available in the current record because the manuscript is no longer accessible. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
The cervical cancer patient’s death rate can be minimized by accurate and early detection of cervical cancer (CC). One of the popular techniques called the Pap test or Pap smear is widely used for the early detection of CC. The manual analysis consumed more time in the case of CC detection. The existing techniques met few shortcomings in terms of poor accuracy, more computational complexity, higher feature dimensionality, poor reliability, and higher time-consumption with poor hyperparameters optimization. Hence, the computer-aided diagnostic model provides reliable and accurate CC detection at the initial stage. In this paper, we proposed MASO optimized DenseNet 121 architecture for the early detection of cervical cancer. At first, different kinds of augmentation techniques such as horizontal flip, vertical flip, zooming, shearing, height shift, width shift, rotation, and brightness to increase the number of training samples. The Mutation based Atom Search Optimization (MASO) algorithm is established to optimize the hyperparameters in DenseNet 121 architecture suchnumber of neurons in the dense layer, learning rate value, and the batch sizes. Different kinds of performance metrics such as accuracy, specificity, sensitivity, precisions, recall, F-score, and confusion matrix evaluate the performance of MASO optimized DenseNet 121 architecture for CC detection. A single normal class with three abnormal classes namely Carcinoma, Light dysplastic, and Sever dysplastic were selected from the Hervel dataset for experimental investigation. The proposed MASO optimized DenseNet 121 architecture achieves 98.38% accuracy, 98.5% specificity, 98.83% sensitivity, 98.58% precision, 99.3% recall and 98.25% F-score values than other existing methods.
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RETRACTED: Mutation based Atom Search Optimization Algorithm for Hyperparameter Optimization of the DenseNet121 Architecture for Cervical Cancer Classification | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF RETRACTED: Mutation based Atom Search Optimization Algorithm for Hyperparameter Optimization of the DenseNet121 Architecture for Cervical Cancer Classification B Chitra, S.S. Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-302143/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Editorial Note The authors have requested that this preprint be removed from Research Square. Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract The authors have requested that this preprint be removed from Research Square. Cite Share Download PDF Status: Posted Version 1 posted 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. 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