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ISSN 2063-5346
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CERVICAL CANCER DIAGNOSTICS DETECTION USING DEEP LEARNING TECHNIQUES

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Mr. K. Rajasekhar rao, R. Akshaya, J. Shirisha, M. Vishalakshi
» doi: 10.31838/ecb/2023.12.s3.297

Abstract

Perhaps of the most widely recognized sickness influencing ladies, cervical disease is a main source of death in many emerging countries. A pap smear test or acidic corrosive staining (visual assessment) are utilized to analyze cervical sores. A minimal expense screening methodology known as computerized colposcopy yields simple and easy outcomes. In this manner, motorizing cervical sickness Colposcopy imaging screening will be incredibly beneficial in saving many lives. Numerous robotized approaches using PC vision and ML in cervical screening have gathered prominence lately, opening the doorway for cervical threatening development finding. Be that as it may, most of strategies depend exclusively on division comment and location of the cervical spine. This study intends introducing the FSOD-GAN (Faster Small Object Detection Neural Networks) for utilizing advanced colposcopy pictures for cervical screening and cancer recognition. A Faster Region-Based Convolutional Neural Network (FR-CNN) is utilized in proposed strategy to distinguish cervical spots and play out a various leveled multiclass grouping of three distinct types of lesions caused by cervical cancer. The experiment was completed using colposcopy data from openly available sources.On 1,993 patients with three cervical orders, and the prescribed technique had near 100 percent precision in distinctive cervical illness stages.

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