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Title: :  PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM
PaperId: :  23115
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-23115
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
SURUTHI MBANNARI AMMAN INSTITUTE OF TECHNOLOGY
THENDRALMANI J VBANNARI AMMAN INSTITUTE OF TECHNOLOGY
RAJALAKSHMI RBANNARI AMMAN INSTITUTE OF TECHNOLOGY

Abstract

Computer Engineering and research and development
Deep convolutional neural network, Generative Adversarial Network , ultrasound, imaging techniques.
Polycystic ovarian syndrome (PCOS) stands as a significant threat among gynaecological conditions due to its tendency to manifest with subtle symptoms, often leading to diagnosis at advanced stages. Distinguishing between various types of ovarian cysts poses a formidable challenge for medical professionals. Among imaging techniques, ultrasound (US) imaging emerges as the preferred choice owing to its convenience, non-invasiveness, and real-time capabilities. However, the current screening methods for ovarian cysts via imaging still suffer from limitations, contributing to the poor prognosis associated with ovarian cysts. In recent years, the integration of deep learning models with US images has shown promising results in enhancing diagnostic efficiency, reducing mortality rates, and minimizing diagnostic delays. This project introduces an innovative approach for diagnosing ovarian cysts using US images, employing a Deep Convolutional Neural Network (DCNN) model enhanced with a Generative Adversarial Network (GAN) to address overfitting issues by augmenting training samples. Through this augmentation process, a more robust dataset is created, enabling the DCNN model to effectively classify different types of ovarian cysts. The proposed system not only aids in accurate diagnosis but also serves as a valuable tool for physicians in medical decision-making. By analysing the outcomes generated by the fused DCNN model, medical professionals can gain valuable insights into the nature of ovarian cysts and make informed treatment decisions. The results obtained from this study demonstrate the superior precision and accuracy of the proposed model in diagnosing ovarian cancer and other types of cysts. This advancement marks a significant stride towards improving the management and prognosis of ovarian cysts, potentially saving lives through early detection and intervention.

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IJARIIE SURUTHI M, THENDRALMANI J V, and RAJALAKSHMI R. "PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 2690-2698
MLA SURUTHI M, THENDRALMANI J V, and RAJALAKSHMI R. "PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 2690-2698.
APA SURUTHI M, THENDRALMANI J V, & RAJALAKSHMI R. (2024). PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 2690-2698.
Chicago SURUTHI M, THENDRALMANI J V, and RAJALAKSHMI R. "PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 2690-2698.
Oxford SURUTHI M, THENDRALMANI J V, and RAJALAKSHMI R. 'PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 2690-2698. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/PATHOLOGY_DETECTION_OF_PCOS_AND_ITS_SEVERITY_GRADING_USING_MACHINE_LEARNING_ALGORITHM_ijariie23115.pdf (Accessed : ).
Harvard SURUTHI M, THENDRALMANI J V, and RAJALAKSHMI R. (2024) 'PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 2690-2698IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/PATHOLOGY_DETECTION_OF_PCOS_AND_ITS_SEVERITY_GRADING_USING_MACHINE_LEARNING_ALGORITHM_ijariie23115.pdf (Accessed : )
IEEE SURUTHI M, THENDRALMANI J V, and RAJALAKSHMI R, "PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 2690-2698, Mar-App 2024. [Online]. Available: http://ijariie.com/AdminUploadPdf/PATHOLOGY_DETECTION_OF_PCOS_AND_ITS_SEVERITY_GRADING_USING_MACHINE_LEARNING_ALGORITHM_ijariie23115.pdf [Accessed : ].
Turabian SURUTHI M, THENDRALMANI J V, and RAJALAKSHMI R. "PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver SURUTHI M, THENDRALMANI J V, and RAJALAKSHMI R. PATHOLOGY DETECTION OF PCOS AND ITS SEVERITY GRADING USING MACHINE LEARNING ALGORITHM. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 2690-2698. Available from: http://ijariie.com/AdminUploadPdf/PATHOLOGY_DETECTION_OF_PCOS_AND_ITS_SEVERITY_GRADING_USING_MACHINE_LEARNING_ALGORITHM_ijariie23115.pdf
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