Region‐Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network

Hema, L. K.; Ramachandran, Manikandan; Alhomrani, Majid; Pradeep, N.; Alamri, Abdulhakeem S.; Sharma, Shakti; Alhassan, Musah

doi:10.1155/2022/5968939

Cited by 12 publications

(2 citation statements)

References 37 publications

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“…In Thangamma et al [ 15 ], the k-means algorithm and fuzzy c-means algorithm were used on ultrasound images of ovaries. It was concluded that the fuzzy c-means algorithm provided a better result than the k-means algorithm The work by Hema et al [ 16 ] involved FaRe-ConvNN, which applied annotations on the image dataset, where the images had three categories: epithelial, germ and stroma cells. In order to avoid overfitting and other issues due to the small dataset size, image augmentation using image enhancement and transformation techniques like resizing, masking, segmentation, normalization, vertical or horizontal flips and rotation was undertaken.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks

Kodipalli,

Fernandes,

Gururaj

et al. 2023

Diagnostics

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Difficulty in detecting tumours in early stages is the major cause of mortalities in patients, despite the advancements in treatment and research regarding ovarian cancer. Deep learning algorithms were applied to serve the purpose as a diagnostic tool and applied to CT scan images of the ovarian region. The images went through a series of pre-processing techniques and, further, the tumour was segmented using the UNet model. The instances were then classified into two categories—benign and malignant tumours. Classification was 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 and XGBoosting. DenseNet 121 emerges as the best model on this dataset 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 with common machine learning algorithms, with and without optimization techniques applied.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks

Kodipalli,

Fernandes,

Gururaj

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…The work by Hema et.al. [16] involves FaRe-ConvNN which applies annotations on the image dataset where the images have three categories: epithelial, germ and stroma cells. In order to avoid overfitting and other issues due to small dataset size, image augmentation using image enhancement and transformation techniques like resizing, masking, segmentation, normalization, vertical or horizontal flips and rotation is done.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks

Kodipalli¹,

Gururaj²,

VR³

et al. 2023

Preprint

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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.

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Ovarian Tumor Diagnosis and Characterization of CT Scan Images Using Ensemble Deep Learning and Explainable AI

Kodipalli,

Colaco,

Dasar

et al. 2024

Lecture Notes in Networks and Systems

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Region‐Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network

Cited by 12 publications

References 37 publications

Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks

Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks

Performance Analysis of Segmentation and Classification of CT scanned Ovarian Tumours using UNet and Deep Convolutional Neural Networks

Ovarian Tumor Diagnosis and Characterization of CT Scan Images Using Ensemble Deep Learning and Explainable AI

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