Mobile QR Code QR CODE

2024

Acceptance Ratio

21%

REFERENCES

1 
A. M. Parekh and N. B. Shah, ``Classification of ovarian cyst using soft computing technique,'' Proc. of 8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017, no. September, 2017.DOI
2 
S. Mobeen and R. Apostol, ``Ovarian cyst,'' National Library of Medicine, 2022. https://www.ncbi.nlm.nih.gov/books/NBK560541/(accessed Nov. 20, 2023).URL
3 
A. A. Alzubi, H. Al Moghrabi, M. A. Alzubi, S. A. Alzubi, and S. Arabia, ``Methods for Automatic Cyst Detection and Classification in Ultrasound Images of the Female Genitalia Using Image Processing,'' Journal of Population Therapeutics and Clinical Pharmacology, vol. 30, no. 6, pp. 297-305, 2023.DOI
4 
B. Mahesh, ``Machine learning algorithms - A review,'' International Journal of Science and Research, vol. 9, no. 1, January 2020, pp. 381-386, 2020.DOI
5 
A. L. Tarca, V. J. Carey, X. Chen, R. Romero, and S. Drǎghici, ``Machine learning and its applications to biology.,'' PLoS Computational Biology, vol. 3, no. 6. 2007.DOI
6 
J. Alzubi, A. Nayyar, and A. Kumar, ``Machine learning from theory to algorithms,'' Journal of Physics: Conference Series, 2018.DOI
7 
P. Raja and P. Suresh, ``Variety of ovarian cysts detection and classification using 2D convolutional neural network,'' Multimedia Tools and Applications, vol. 83, no. 16, pp. 49473-49491, 2024.DOI
8 
P. Hu, Y. Gao, Y. Zhang, and K. Sun, ``Ultrasound image-based deep learning to differentiate tubal-ovarian abscess from ovarian endometriosis cyst,'' Frontiers in Physiology, vol. 14, pp. 1-9, 2023.DOI
9 
J. Fan, J. Liu, Q. Chen, W. Wang, and Y. Wu, ``Accurate ovarian cyst classification with a lightweight deep learning model for ultrasound images,'' IEEE Access, vol. 11, pp. 110681-110691, 2023.DOI
10 
T. N. Ravishankar, H. Makarand Jadhav, N. Satheesh Kumar, S. Ambala, and M. Pillai N, ``A deep learning approach for ovarian cysts detection and classification (OCD-FCNN) using fuzzy convolutional neural network,'' Measurement: Sensors, vol. 27, no. May, 100797, 2023.DOI
11 
C. Narmatha, P. Manimegalai, J. Krishnadass, P. Valsalan, S. Manimurugan, and M. Mustafa, ``Ovarian cysts classification using novel deep reinforcement learning with Harris Hawks optimization method,'' Journal of Supercomputing, vol. 79, no. 2, pp. 1374-1397, 2023.DOI
12 
Y. Suganya, S. Ganesan, and P. Valarmathi, ``Ultrasound ovary cyst image classification with deep learning neural network with Support vector machine,'' International Journal of Health Sciences, vol. 6, pp. 8811-8818, 2022.DOI
13 
R. Benazir Begam, V. Yogalakshmi, G. Saranya, D. Gururaj, S. Jagtap, and V. Ravanan, ``Ovarian cyst detection using neural networks,'' Proc. of the International Conference on Electronics and Renewable Systems, ICEARS 2022, pp. 1827-1830, 2022.DOI
14 
S. Srivastava, P. Kumar, V. Chaudhry, and A. Singh, ``Detection of ovarian cyst in ultrasound images using fine-tuned VGG-16 deep learning network,'' SN Computer Science, vol. 1, no. 2, pp. 1-8, 2020.DOI
15 
K. Simonyan and A. Zisserman, ``Very deep convolutional networks for large-scale image recognition,'' Proc. of International Conference on Learning Representations, pp. 1-14, 2014.DOI
16 
K. He, X. Zhang, S. Ren, and J. Sun, ``Deep residual learning for image recognition,'' Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.DOI
17 
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, ``Densely connected convolutional networks,'' Proc. of 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 2261-2269, 2017.DOI
18 
M. Tan and Q. V. Le, ``EfficientNet: Rethinking model scaling for convolutional neural networks,'' Proc. of 36th International Conference on Machine Learning, ICML 2019, pp. 10691-10700, 2019.DOI
19 
Y. Wang, G. Huang, S. Song, X. Pan, Y. Xia, and C. Wu, ``Regularizing deep networks with semantic data augmentation,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3733-3748, 2022.DOI
20 
B. Kayalibay, G. Jensen, and P. van der Smagt, ``CNN-based segmentation of medical imaging data,'' arXiv preprint arXv:1701.03056, 2017DOI
21 
G. Litjen, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompo, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60-88, 2017.DOI
22 
S. E. Nassar, I. Yasser, H. M. Amer, and M. A. Mohamed, ``A robust MRI-based brain tumor classification via a hybrid deep learning technique,'' Journal of Supercomputing, vol. 80, no. 2, pp. 2403-2427, 2024.DOI
23 
A. Kodipalli, S. L. Fernandes, and S. Dasar, ``An empirical evaluation of a novel ensemble deep neural network model and explainable AI for accurate segmentation and classification of ovarian tumors using CT images,'' Diagnostics, vol. 14, no. 5, 2024.DOI
24 
A. Kodipalli, S. L. Fernandes, V. Gururaj, S. V. Rameshbabu, and S. Dasar, ``Performance analysis of segmentation and classification of CT-scanned ovarian tumours using U-Net and deep convolutional neural networks,'' Diagnostics, vol. 13, no. 13, 2023.DOI
25 
R. V. Manjunath, A. Ghanshala, and K. Kwadiki, ``Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images,'' Multimedia Tools and Applications, vol. 83, no. 1, pp. 2773-2790, 2024.DOI
26 
C. Kamala and J. M. Shivaram, ``Segmentation of ovarian cyst using improved U-NET and hybrid deep learning model,'' Multimedia Tools and Applications, vol. 83, no. 14, pp. 42645-42679, 2024.DOI
27 
A. Bruno, G. Ignesti, O. Salvetti, D. Moroni, and M. Martinelli, ``Efficient lung ultrasound classification,'' Bioengineering, vol. 10, no. 5, 2023.DOI
28 
F. R. Eweje, B. Bao, J. Wu, D. Dalal, W. Liao, Y. He, Y. Luo, S. Lu, P. Zhang, X. Peng, R. Sebro, H. X. Bai, and L. States, ``Deep Learning for Classification of Bone Lesions on Routine MRI,'' EBioMedicine, vol. 68, 2021, doi: 10.1016/j.ebiom.2021.103402.DOI
29 
M. Kalaiyarasi, R. Dhanasekar, S. Sakthiya Ram, and P. Vaishnavi, ``Classification of benign or malignant tumor using machine learning,'' IOP Conference Series: Materials Science and Engineering, vol. 995, no. 1, 2020.DOI