Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

Nagarajan, P; Tajunisha, N.

doi:10.18280/ria.350401

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…In the proposed work by Goodfellow et al [ 10 ], an adversarial net framework was suggested that loosely resembles a minimax two-player game. Nagarajan et al [ 11 ] and Zhao et al [ 12 ], in their research work, provided three approaches that were used to classify ovarian cancer types using CT images. The first approach used a deep convolutional neural network (DCNN) based on AlexNet, which did not provide satisfactory results.…”

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

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show abstract

“…al. [11], in their research work, provide three approaches that are used to classify ovarian cancer types using CT images. The first approach uses a deep convolutional neural network (DCNN) based on AlexNet which does not provide satisfactory results.…”

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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“…Nagarajan and Tajunisha [16] talked about the sonographic characteristics of different ovarian cystic masses that can be discovered inside or outside the ovary. Ovarian cysts typically come in seven different forms.…”

Section: Related Workmentioning

confidence: 99%

“…The main source of evidence for the efficiency of the suggested strategy was our collection of images of ovarian tumors. As there isn't a publicly accessible liver image dataset, one was used instead to compare with current medical picture segmentation techniques [16]. Zhejiang University's School of Medicine's Affiliated Women's Hospital offered an ovarian tumor dataset used in this work, which includes 196 Ultrasound Images (UI) in total, as shown in Figure 2 some examples from images.…”

Section: Datasetsmentioning

confidence: 99%

Early Detection and Segmentation of Ovarian Tumor Using Convolutional Neural Network with Ultrasound Imaging

Alwan,

Kadhim,

Issa

et al. 2023

RIA

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The clinical differentiation between benign and malignant ovarian tumors represents a substantial challenge in the fields of obstetrics and gynecology, particularly following the detection of ovarian cysts through ultrasound. Ovarian cancers, diverse in type, often exhibit overlapping characteristics that complicate diagnosis. In this study, a deep learningbased methodology was developed to aid in the rapid and accurate differentiation of ovarian cancer types using ultrasound imaging. A deep learning approach, utilizing transfer learning with Convolutional Neural Network (CNN) models, was employed. To ensure the stability and robustness of the solution, ten iterations of training and validation were executed, with data randomly sampled for each iteration. The mean of the ten iterations' outcomes constituted the final evaluation metric. Initially, ultrasound images were enhanced to augment the quality of the training dataset, followed by the extraction of low-level texture features for the segmentation of images. Subsequently, ten established CNN models were utilized for both training and transfer learning processes. In the culmination of the study, a multitask model was proposed, capable of concurrently executing detection and segmentation tasks. The conducted evaluations reveal that the deep learning models can classify ovarian tumors with an accuracy of 98.79%, a rate comparable to that of skilled medical practitioners.

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Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network

Cited by 4 publications

References 16 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

Early Detection and Segmentation of Ovarian Tumor Using Convolutional Neural Network with Ultrasound Imaging

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