Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks
Abstract
:1. Introduction
2. Literature Review
2.1. Medical Imaging Classification Using CNN
2.2. Medical Imaging Classification Using Ensemble Deep Learning
2.3. Deep Learning in Medical Imaging Segmentation
3. Methodology
3.1. Dataset Description
3.2. Segmentation Using U-Net Model
3.3. Detailed Methodology
4. Results and Discussions
4.1. Experimental Settings
4.2. Segmentation Results Using the UNet Model
4.3. Classification Results Using Variants of the CNN Model
5. Conclusions and Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Image | Benign | |||
---|---|---|---|---|
Class0_Dice | Class1_Dice | Class0_Jaccard | Class1_Jaccard | |
CT_1 | 0.99756 | 0.98193 | 0.99514 | 0.96451 |
CT_2 | 0.99775 | 0.98975 | 0.99552 | 0.97972 |
CT_3 | 0.99758 | 0.98710 | 0.99518 | 0.97454 |
CT_4 | 0.99738 | 0.98974 | 0.99479 | 0.97969 |
CT_5 | 0.99777 | 0.97334 | 0.99556 | 0.94807 |
CT_6 | 0.99701 | 0.98348 | 0.99404 | 0.96751 |
CT_7 | 0.99761 | 0.97269 | 0.99523 | 0.94684 |
CT_8 | 0.99729 | 0.99024 | 0.99459 | 0.98068 |
CT_9 | 0.99895 | 0.98740 | 0.99790 | 0.97512 |
CT_10 | 0.99882 | 0.92817 | 0.99765 | 0.86597 |
CT_11 | 0.99786 | 0.97470 | 0.99577 | 0.95066 |
CT_12 | 0.99814 | 0.97956 | 0.99637 | 0.95994 |
CT_13 | 0.99862 | 0.96864 | 0.99724 | 0.93920 |
CT_14 | 0.99718 | 0.95798 | 0.99439 | 0.91935 |
CT_15 | 0.99460 | 0.88555 | 0.98926 | 0.89461 |
CT_16 | 0.99696 | 0.96709 | 0.99395 | 0.93623 |
CT_17 | 0.99686 | 0.96678 | 0.99375 | 0.93569 |
CT_18 | 0.99777 | 0.96998 | 0.99553 | 0.94171 |
CT_19 | 0.99643 | 0.94171 | 0.99290 | 0.88984 |
CT_20 | 0.99724 | 0.96965 | 0.99450 | 0.94101 |
Image | Malignant | |||
---|---|---|---|---|
Class0_Dice | Class1_Dice | Class0_Jaccard | Class1_Jaccard | |
CT_1 | 0.99485 | 0.917209 | 0.989753 | 0.847079 |
CT_2 | 0.996533 | 0.950731 | 0.993091 | 0.906089 |
CT_3 | 0.994843 | 0.912769 | 0.989738 | 0.839535 |
CT_4 | 0.996412 | 0.881644 | 0.992849 | 0.788339 |
CT_5 | 0.998503 | 0.9179 | 0.99701 | 0.848258 |
CT_6 | 0.99661 | 0.951691 | 0.993242 | 0.907835 |
CT_7 | 0.997797 | 0.927598 | 0.995603 | 0.864972 |
CT_8 | 0.997502 | 0.969651 | 0.995017 | 0.941091 |
CT_9 | 0.996653 | 0.961937 | 0.993328 | 0.926665 |
CT_10 | 0.995213 | 0.926677 | 0.990471 | 0.863373 |
CT_11 | 0.983532 | 0.836238 | 0.967598 | 0.718565 |
CT_12 | 0.995118 | 0.866793 | 0.990284 | 0.764902 |
CT_13 | 0.999053 | 0.966729 | 0.998107 | 0.9356 |
CT_14 | 0.995317 | 0.861702 | 0.990677 | 0.757008 |
CT_15 | 0.997093 | 0.952554 | 0.994203 | 0.909406 |
CT_16 | 0.997117 | 0.965229 | 0.99425 | 0.932794 |
CT_17 | 0.995457 | 0.727209 | 0.990954 | 0.57135 |
CT_18 | 0.986163 | 0.701614 | 0.972704 | 0.540374 |
CT_19 | 0.998494 | 0.914519 | 0.996992 | 0.842501 |
CT_20 | 0.998775 | 0.918228 | 0.997552 | 0.848818 |
Sl. No | CNN Architectures | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|---|
1. | CNN | 89.7% | 89.1% | 88.0% | 89.4% |
2. | ResNet 152 | 92.7% | 92.4% | 91.5% | 92.7% |
3. | DenseNet121 | 95.7% | 95.2% | 94.3% | 95.6% |
4. | Inception-ResNet V4 | 94.3% | 94.1% | 93.2% | 94.2% |
5. | VGG 16 | 91.5% | 91.6% | 90.5% | 91.4% |
6. | Xception | 87.2% | 87.3% | 86.7% | 86.3% |
Sl. No | Ensemble ML Models | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|---|
1. | Random Forest | 82.4% | 82.7% | 83.6% | 81.5% |
2. | Gradient boosting | 80.9% | 80.3% | 80.88% | 79.5% |
3. | AdaBoosting | 89.4% | 88.5% | 89.1% | 87.6% |
4. | XGBoosting | 79.5% | 80.2% | 79.8% | 80.3% |
Sl. No | Ensemble ML Models | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|---|
1. | Random Forest | 86.3% | 86.3% | 87.9% | 86.2% |
2. | Gradient boosting | 82.7% | 82.7% | 83.7% | 81.3% |
3. | AdaBoosting | 91.37% | 91.87% | 90.3% | 91.78% |
4. | XGBoosting | 80.34% | 81.67% | 80.48% | 81.78% |
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Kodipalli, A.; Fernandes, S.L.; Gururaj, V.; Varada Rameshbabu, S.; Dasar, S. Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks. Diagnostics 2023, 13, 2282. https://doi.org/10.3390/diagnostics13132282
Kodipalli A, Fernandes SL, Gururaj V, Varada Rameshbabu S, Dasar S. Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks. Diagnostics. 2023; 13(13):2282. https://doi.org/10.3390/diagnostics13132282
Chicago/Turabian StyleKodipalli, Ashwini, Steven L. Fernandes, Vaishnavi Gururaj, Shriya Varada Rameshbabu, and Santosh Dasar. 2023. "Performance Analysis of Segmentation and Classification of CT-Scanned Ovarian Tumours Using U-Net and Deep Convolutional Neural Networks" Diagnostics 13, no. 13: 2282. https://doi.org/10.3390/diagnostics13132282