Comparative analysis of active contour random walker and watershed algorithms in segmentation of ovarian cancer

Ruchitha, P J; Sai, Richitha Y; Kodipalli, Ashwini; Martis, Roshan Joy; Dasar, Santosh; Ismail, Taha

doi:10.1109/discover55800.2022.9974855

Cited by 33 publications

(6 citation statements)

References 20 publications

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“…After the region-based training in FaRe-ConvNN, a combination of SVC and Gaussian NB classifiers was used to classify the images, which resulted in impressive precision and recall values [ 17 ]. In the works carried out by Ashwini et al [ 18 , 19 , 20 ], various Deep Learning models were used to segment the CT scanned images and classify them using variants of CNN. In the work [ 18 , 19 ], Otsu’s method was used to segment the tumour and a dice score of 0.82 and Jaccard score of 0.8356 were obtained.…”

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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“…In the works carried out by Ashwini et al [18][19][20], the various deep learning models were used to segment the CT scanned images and classified using variants of CNN. In the work [18][19], the Otsu's method was used to segment the tumor and obtained the dice score of 0.82 and Jaccard score of 0.8356.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the work [18][19], the Otsu's method was used to segment the tumor and obtained the dice score of 0.82 and Jaccard score of 0.8356. Further the performance of the segmentation was used cGAN [20], in this study the segmentation and classification of tumors were carried out in the single pipeline and obtained the dice score of 0.91 and the Jaccard score of 0.89.…”

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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“…The author obtained an accuracy of 90.2% using these ML models. Kodipalli & Devi, 2023;Kodipalli, Devi, et al, 2022;Kodipalli, Guha, et al, 2022;Kodipalli, Gururaj, et al, 2023;Ruchitha et al, 2022), contributed extensively to the detection of PCOS using a questionnaire and found that Fuzzy TOPSIS outperformed the SVM algorithm (Kodipalli & Devi, 2021). Watershed and active contour random walker were used in (Ruchitha et al, 2022) for segmenting the ovarian tumour and it was found that the watershed algorithm outperformed the active contour random walker algorithm.…”

Section: Ovarian Cancer Using Machine Learning Algorithmsmentioning

confidence: 99%

“…Kodipalli & Devi, 2023;Kodipalli, Devi, et al, 2022;Kodipalli, Guha, et al, 2022;Kodipalli, Gururaj, et al, 2023;Ruchitha et al, 2022), contributed extensively to the detection of PCOS using a questionnaire and found that Fuzzy TOPSIS outperformed the SVM algorithm (Kodipalli & Devi, 2021). Watershed and active contour random walker were used in (Ruchitha et al, 2022) for segmenting the ovarian tumour and it was found that the watershed algorithm outperformed the active contour random walker algorithm. The mental condition of women suffering from ovarian cancer was analysed in Devi, 2023 and) and it was found that women with ovarian cancer have more mental problems compared to women without ovarian cancer.…”

Section: Ovarian Cancer Using Machine Learning Algorithmsmentioning

confidence: 99%

Semantic segmentation and classification of polycystic ovarian disease using attention UNet, Pyspark, and ensemble learning model

Kodipalli,

Devi,

Dasar

2023

Expert Systems

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Ovarian abnormality like polycystic ovarian disease (PCOD) is one of the most common diseases among women worldwide. PCOD not only has an impact on infertility but also hurts the psychological well‐being of women affecting their quality of life. In this study, a two‐class pattern learning problem is designed for the classification of PCOD. In total, 37 clinical parameters and abdominal ultrasound images of women are collected under the proper ethical protocol. Using only clinical data, an accuracy of 93.7% is obtained using Random Forest as the classifier which is further improved to 95.54% by using a Randomized Search CV during Random Forest classification. The ultrasound images are classified using the proposed Attention‐UNet architecture and a mean Dice score of 0.945 is obtained indicating more accurate segmentation. The segmented images are passed through the state‐of‐the‐art EfficientNet B6 for the classification of PCOS and non‐PCOS and recorded an accuracy of 95.47%. Using big data architecture Pyspark, the performance is further enhanced to 96.8% and 96.3% for clinical and ultrasound images respectively along with the reduced computational speed. The results of these classifiers are then used to create metadata and a customized Artificial Neural Network is applied for the final prediction of PCOD and non‐PCOD. From the results, it can be seen that the stacking model outperformed with an accuracy of 98.12% when compared to the single classifier. Our proposed method has very good performance with less computation, contributing a new architecture to evaluate PCOD and hence helping to improve the wellness of women.

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Comparative analysis of active contour random walker and watershed algorithms in segmentation of ovarian cancer

Cited by 33 publications

References 20 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

Semantic segmentation and classification of polycystic ovarian disease using attention UNet, Pyspark, and ensemble learning model

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