Integrated Gaussian—GLCM Butterfly Optimization with CNN (IGGBOCNN): A Hybrid Approach for Ovarian Cancer Classification in Medical Image Analysis
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3601-0_6
Reference10 articles.
1. Wang R, Li R, Lei Y, Zhu Q (2015) Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging. Biomed Mater Eng 26(Suppl 1):S975–81. https://doi.org/10.3233/BME-151392
2. Shetty A, Shah V (2018) Cervical cancer prediction using machine learning: a comparative approach. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT), Bengaluru, India, pp 1–6, https://doi.org/10.1109/ICCCNT.2018.8494169.
3. Huang CL, Lian MJ, Wu YH, Chen WM, Chiu WT (2020) Human ovarian adenocarcinoma cells with cisplatin-resistance by feature extraction of gray level co-occurrence matrix using optical images. Diagnostics (Basel). J10(6):389. https://doi.org/10.3390/diagnostics10060389
4. Koray Aslan B, Anıl Onan M, Yilmaz C, Bukan N, Erdem M (2020) Comparison of HE 4, CA 125, ROMA score and ultrasound score in the differential diagnosis of ovarian masses. J Gynecol Obstet Human Reprod 49(5):101713
5. Aditya M, Amrita I, Kodipalli A, Martis RJ (2021) Ovarian cancer detection and classification using machine learning. In: 2021 5th international conference on electrical, electronics, communication, computer technologies and optimization techniques (ICEECCOT), Mysuru, India, pp 279–282. https://doi.org/10.1109/ICEECCOT52851.2021.9707954
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