Version 1
: Received: 13 April 2023 / Approved: 13 April 2023 / Online: 13 April 2023 (10:50:53 CEST)
How to cite:
Kodipalli, A.; Gururaj, V.; VR, S.; Fernandes, S.L.; Dasar, S. Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks. Preprints2023, 2023040320. https://doi.org/10.20944/preprints202304.0320.v1
Kodipalli, A.; Gururaj, V.; VR, S.; Fernandes, S.L.; Dasar, S. Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks. Preprints 2023, 2023040320. https://doi.org/10.20944/preprints202304.0320.v1
Kodipalli, A.; Gururaj, V.; VR, S.; Fernandes, S.L.; Dasar, S. Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks. Preprints2023, 2023040320. https://doi.org/10.20944/preprints202304.0320.v1
APA Style
Kodipalli, A., Gururaj, V., VR, S., Fernandes, S.L., & Dasar, S. (2023). Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks. Preprints. https://doi.org/10.20944/preprints202304.0320.v1
Chicago/Turabian Style
Kodipalli, A., Steven L Fernandes and Santosh Dasar. 2023 "Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks" Preprints. https://doi.org/10.20944/preprints202304.0320.v1
Abstract
The difficulty in detecting tumors in earlier stages is the major cause of mortalities of patients, despite the advancements in treatment and research regarding ovarian cancer. Deep Learning algorithms are applied to serve the purpose of a diagnostic tool by applying them on CT scan images of the ovarian region. The images go through a series of pre-processing techniques and further the tumor is segmented using the UNet model. Instances are then classified into two categories – benign and malignant tumors. Classification is performed using Deep Learning models like CNN, ResNet, DenseNet, Inception-ResNet, VGG16 and Xception along with Machine Learning models such as Random Forest, Gradient Boosting, AdaBoosting, XGBoosting. DenseNet 121 emerges as the best model on this dataset even after applying optimization on the Machine Learning models by obtaining an accuracy of 95.7%. The current work demonstrates the comparison of multiple CNN architectures among themselves and with common Machine Learning algorithms, with and without optimization techniques applied.
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
