Abirami C, Harikumar R, Chakravarthy SS. 2016. Efficiency evaluation and detection of micro calcification in digital mammograms utilizing wavelet options. In 2016 Worldwide Convention on Wi-fi Communications, Sign Processing and Networking (WiSPNET) (pp. 2327–2331). IEEE.
SR SC, Rajaguru H. 2021. A Systematic Evaluation on Screening, Analyzing and Classification of Breast Most cancers. 2021 Good Applied sciences, Communication and Robotics (STCR), pp.1–4.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. International most cancers statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 nations. Most cancers J Clin. 2021;71(3):209–49.
Sannasi Chakravarthy SR, Rajaguru H. Detection and classification of microcalcification from digital mammograms with firefly algorithm, excessive studying machine and non-linear regression fashions: a comparability. Int J Imaging Syst Technol. 2020;30(1):126–46.
Naji MA, El Filali S, Bouhlal M, Benlahmar EH, Abdelouhahid RA, Debauche O. Breast Most cancers prediction and prognosis by a New Method primarily based on Majority Voting Ensemble Classifier. Procedia Comput Sci. 2021;191:481–6.
Chakravarthy SS, Rajaguru H. Automated detection and classification of mammograms utilizing improved excessive studying machine with deep studying. IRBM. 2022;43(1):49–61.
Faisal MI, Bashir S, Khan ZS, Khan FH. 2018. An analysis of machine studying classifiers and ensembles for early stage prediction of lung most cancers. In 2018 third worldwide convention on rising developments in engineering, sciences and expertise (ICEEST) (pp. 1–4). IEEE.
Mughal B. Early Detection and Classification of Breast Tumor From Mammography (Doctoral dissertation, COMSATS Institute of Info Expertise, Islamabad). 2019.
Wei B, Han Z, He X, Yin Y. 2017, April. Deep studying mannequin primarily based breast most cancers histopathological picture classification. In 2017 IEEE 2nd worldwide convention on cloud computing and massive information evaluation (ICCCBDA) (pp. 348–353). IEEE.
Khuriwal N, Mishra N. 2018, March. Breast most cancers prognosis utilizing adaptive voting ensemble machine studying algorithm. In 2018 IEEMA engineer infinite convention (eTechNxT) (pp. 1–5). IEEE.
Thuy MBH, Hoang VT. Fusing of deep studying, switch studying and gan for breast most cancers histopathological picture classification. In: Worldwide Convention on Pc Science, Utilized Arithmetic and Purposes. Cham: Springer; 2019. p. 255–66.
Bhowal P, Sen S, Velasquez JD, Sarkar R. Fuzzy ensemble of deep studying fashions utilizing Choquet fuzzy integral, coalition sport and knowledge idea for breast most cancers histology classification. Knowledgeable Syst Appl. 2022;190:116167.
Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC. A novel deep studying primarily based framework for the detection and classification of breast most cancers utilizing switch studying. Sample Recognit Lett. 2019;125:1–6.
Muduli D, Sprint R, Majhi B. Automated prognosis of breast most cancers utilizing multi-modal datasets: a deep convolution neural community primarily based strategy. Biomed Sign Course of Management. 2022;71:102825.
Mahesh TR, Vinoth Kumar V, Dhilip Kumar V, Geman O, Margalam M, Guduri M. The stratified Okay-folds cross-validation and class-balancing strategies with high-performance ensemble classifiers for breast most cancers classification. Healthcare Analytics. 2023. 100247,ISSN 2772–4425. https://doi.org/10.1016/j.well being.2023.100247.
Sannasi Chakravarthy SR, Rajaguru H. SKMAT-U-Web structure for breast mass segmentation. Int J Imaging Syst Technol. 2022;1–9. https://doi.org/10.1002/ima.22781.
Debelee TG, Schwenker F, Ibenthal A, Yohannes D. Survey of deep studying in breast most cancers picture evaluation. Evol Syst. 2020;11(1):143–63.
Sannasi Chakravarthy SR, Bharanidharan N, Rajaguru H. 2022. Multi-deep CNN primarily based experimentations for early prognosis of breast most cancers. IETE J Res, pp.1–16.
Moura DC, López MAG, Cunha P, Posada NGD, Pollan RR, Ramos I, Loureiro JP, Moreira IC, Araújo BM, Fernandes TC. November. Benchmarking datasets for breast most cancers computer-aided prognosis (CADx). Iberoamerican Congress on Sample Recognition. Berlin, Heidelberg: Springer; 2013. pp. 326–33.
Suckling JP. 1994. The mammographic picture evaluation society digital mammogram database. Digital Mammo, pp.375–386.
Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. Inbreast: towards a full-field digital mammographic database. Acad Radiol. 2012;19(2):236–48.
Heath M, Bowyer Okay, Kopans D, Kegelmeyer P, Moore R, Chang Okay, Munishkumaran S. Present standing of the digital database for screening mammography. Digital mammography. Dordrecht: Springer; 1998. pp. 457–60.
Sannasi Chakravarthy SC, Rajaguru H. Lung most cancers detection utilizing probabilistic neural community with modified crow-search algorithm. Asian Pac J Most cancers Prev. 2019;20(7):2159.
Abd Elaziz M, Heidari AA, Fujita H, Moayedi H. A aggressive chain-based Harris Hawks optimizer for world optimization and multi-level picture thresholding issues. Appl Gentle Comput. 2020;95:106347.
Weiss Okay, Khoshgoftaar TM, Wang D. A survey of switch studying. J Huge information. 2016;3(1):1–40.
Simonyan Okay, Zisserman A. 2014. Very deep convolutional networks for large-scale picture recognition. arXiv Preprint arXiv:14091556.
Sannasi Chakravarthy SR, Bharanidharan N, Rajaguru H. A scientific assessment on machine studying algorithms used for forecasting lake-water degree fluctuations. Concurrency and Computation: Apply and Expertise; 2022. p. e7231.
Theckedath D, Sedamkar RR. Detecting have an effect on states utilizing VGG16, ResNet50 and SE-ResNet50 networks. SN Comput Sci. 2020;1(2):1–7.
Xia X, Xu C, Nan B. 2017. Inception-v3 for flower classification. In 2017 2nd worldwide convention on picture, imaginative and prescient and computing (ICIVC) (pp. 783–787). IEEE.
Dong N, Zhao L, Wu CH, Chang JF. Inception v3 primarily based cervical cell classification mixed with artificially extracted options. Appl Gentle Comput. 2020;93:106311.
Dong WM, Wong FS. Fuzzy weighted averages and implementation of the extension precept. Fuzzy Units Syst. 1987;21(2):183–99.
Sugeno M. An introductory survey of fuzzy management. Inf Sci. 1985;36(1–2):59–83.
Liao J, Wu S, Du T. The Sugeno integral with respect to α-preinvex capabilities. Fuzzy Units Syst. 2020;379:102–14.
Iliev AI, Kyurkchiev N, Markov S. On the approximation of the minimize and step capabilities by logistic and Gompertz capabilities. Biomath. 2015;4(2):ppID-1510101.
Haji SH, Abdulazeez AM. Comparability of optimization methods primarily based on gradient descent algorithm: a assessment. PalArch’s J Archaeol Egypt/Egyptology. 2021;18(4):2715–43.
Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R. Fuzzy rank-based fusion of CNN fashions utilizing Gompertz operate for screening COVID-19 CT-scans. Sci Rep. 2021;11(1):1–12.
Mahesh TR, Vinoth Kumar V, Muthukumaran V, Shashikala HK, Swapna B. Suresh Guluwadi, Efficiency Evaluation of XGBoost Ensemble Strategies for Survivability with the Classification of Breast Most cancers. J Sensors. 2022;2022:8. https://doi.org/10.1155/2022/4649510. Article ID 4649510.
Rajaraman S, Antani SK. Modality-specific deep studying mannequin ensembles towards bettering TB detection in chest radiographs. IEEE Entry. 2020;8:27318–26.
Mahesh TR, Vinoth Kumar V, Vivek V, et al. Early predictive mannequin for breast most cancers classification utilizing blended ensemble studying. Int J Syst Assur Eng Manag. 2022. https://doi.org/10.1007/s13198-022-01696-0.
Sahu B, Panigrahi A, Rout SK. DCNN-SVM: a brand new strategy for lung most cancers detection. Latest advances in laptop primarily based Techniques, processes and functions. CRC; 2020. pp. 97–105.
Sahu B, Mohanty S, Rout S. A hybrid strategy for breast most cancers classification and prognosis. EAI Endorsed Trans Scalable Inform Syst. 2019;6(20).