Classification of Gall Bladder Cancer Using Ensemble Learning and LSTM
BME 4000 Project/Thesis
Presented by,
Sourav Basak Shuvo
Roll: 1815010
4th Year, 2nd Semester
Dept. of Biomedical Engineering
Khulna University of Engineering & Technology
Supervisor
Dr. Mostafa Zaman Chowdhury
Professor
Dept. of Electrical and Electronic Engineering
Khulna University of Engineering & Technology, Khulna-9203
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Content | Page number |
Introduction | 3-10 |
Literature Review | 11 |
Proposed Methodology | 12-19 |
Results | 20-32 |
Discussion | 33-34 |
Conclusion | 35 |
References | 36 |
Presentation Outline
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Introduction
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Background
Fig. 1. Gallbladder.
Fig. 2. Normal gallbladder.
Fig. 3. Benign gallbladder.
Fig. 4. Malignant gallbladder.
Liver
Stomach
Bile
Gallbladder
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Motivation
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Problem Statement
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Thesis Objectives
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Contribution
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Learning Outcomes
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Publication
S. B. Shuvo and M. Z. Chowdhury, “Classification of Gallbladder Cancer using Average Ensemble Learning,” in proc. of 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), Dhaka, Bangladesh, 2024. [Under Review]
[1]
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Literature Review
Ref. | Year | Main Contributions |
[9] | 2022 | Development of GBCNet to identify GBC by utilizing a multi-scale, 2nd-order pooling architecture. |
[10] | 2022 | The integration of hard negative mining is implemented in the creation of an unsupervised contrastive learning framework for the purpose of training image representations derived from ultrasound videos. |
[11] | 2023 | Provides interpretable explanations consistent with medical literature. |
[12] | 2023 | Demonstrated equivalent or superior diagnosis accuracy compared to skilled radiologists in identifying gallbladder cancer by ultrasound, even when stones, constricted gallbladders, tiny lesions, and neck lesions were present. |
[13] | 2023 | Comparative analysis of the efficacy of deep learning models and a radiologist in distinguishing between XGC and GBC based on ultrasound pictures. |
This Work | Development of a preprocessing pipeline for gallbladder ultrasound images and deployment of an ensemble model combined with LSTM network | |
Table 1. Summary of the recent relevant studies.
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Proposed Methodology
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Proposed Methodology (1/7)
Fig. 5. Gallbladder ultrasound images from GBCU dataset (a) normal, (b) benign and (c) malignant.
Dataset Name | Source | Classes | Number of samples |
GBCU dataset | Basu et al. [9] | 3 | 1255 images: 432 normal, 558 benign, and 265 malignant |
Table 2. Dataset statistics for GBCU dataset.
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Proposed Methodology (2/7)
Fig. 6. Methodology of the proposed work.
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Proposed Methodology (3/7)
Dataset Preprocessing
Fig. 7. Image preprocessing pipeline.
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Proposed Methodology (4/7)
Fig. 8. Splitting GBCU dataset.
Fig. 9. Sample images from the dataset by class; 0 labeled (benign), 1 labeled (malignant), and 2 labeled (normal).
Dataset Splitting
Dataset Labeling
Dataset Augmentation
Fig. 10. Train dataset with augmentation.
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Proposed Methodology (5/7)
Parameters | Values |
Rotation range (in radians) | 0.1 |
Random zoom | 0.2 |
Hyperparameters | Value |
Input Shape | 128 x 128 x 3 |
Early stopping | true |
Batch | 32 |
Patience | 10 |
Metrices | ‘accuracy’ |
Loss Function | ‘sparse_categorical_crossentropy’ |
Monitor | ‘val_accuracy’ |
Optimizer | ‘adamax’ |
Mode | max |
Table 3. Parameters for data augmentation.
Table 4. Hyperparameter settings for proposed model.
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Proposed Methodology (6/7)
Fig. 11. Model architecture of the proposed work.
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Proposed Methodology (7/7)
Stages | Output Shape |
VGG16 (vgg16_block5_conv3) | 8 x 8 x 512 |
VGG19 (vgg19_block5_conv3) | 8 x 8 x 512 |
ResNet50 (resnet50_conv5_block3_out) | 4 x 4 x 2048 |
XceptionNet (xception_block14_sepconv2_act) | 4 x 4 x 2048 |
LSTM Block 1 | 256 |
LSTM Block 2 | 256 |
Concatenate | 512 |
Flatten | 512 |
Dense | 256 |
Dropout | 256 |
Dense | 128 |
Dropout | 128 |
Dense | 64 |
Dense | 3 |
Table 5. The detailed structure of the proposed model.
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Results
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Results (1/12)
Fig. 12. Train and validation accuracy for 5-fold cross validation.
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Results (2/12)
Fig. 13. Train and validation loss for 5-fold cross validation.
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Results (3/12)
Fig. 14. Confusion matrix for all 5 folds (a) fold 1, (b) fold 2, (c) fold 3, (d) fold 4, and (e) fold 5.
(c)
(b)
(e)
(d)
(a)
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Results (4/12)
Fig. 15. Train accuracy for different models.
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Results (5/12)
Fig. 16. Validation accuracy for different models.
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Results (6/12)
Fig. 17. Train loss for different models.
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Results (7/12)
Fig. 18. Validation loss for different models.
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Results (8/12)
Fig. 19. Comparison of ROC curve for class 0 (benign) between different models.
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Results (9/12)
Fig. 20. Comparison of ROC curve for class 1 (malignant) between different models.
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Results (10/12)
Fig. 21. Comparison of ROC curve for class 2 (normal) between different models.
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Results (11/12)
Fig. 22. Bar plot for mean test accuracy & standard deviation between different models.
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Results (12/12)
Fig. 23. Bar plot for performance matrices between different models.
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Discussion (1/2)
Methods | Acc | Loss | F1-score | Sens. | PPV | Kappa | Spec. |
Radiologist A [1] | 70.0 | - | - | 70.7 | - | - | 87.3 |
Radiologist B [1] | 68.3 | - | - | 73.2 | - | - | 81.1 |
GBCNet [1] | 87.7 | - | - | 91.9 | - | - | 96.7 |
Radformer [3] | 90.2 | - | - | 92.9 | - | - | 90.0 |
Our Work | 99.37 | 1.78 | 99.52 | 99.64 | 99.40 | 99.22 | 99.69 |
Table 6. Comparison with other models with GBCU dataset (gallbladder cancer classification)
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Discussion (2/2)
Fig. 24. Comparison with other models with GBCU dataset (gallbladder cancer classification).
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Conclusion
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References
S. Basu, M. Gupta, P. Rana, P. Gupta, and C. Arora, “Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning,” in Proc. of CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, Jun. 2022, pp. 20854–20864.
S. Basu, S. Singla, M. Gupta, P. Rana, P. Gupta, and C. Arora, “Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining,” in Proc. of Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, Cham, 2022, pp. 423–433.
S. Basu, M. Gupta, P. Rana, P. Gupta, and C. Arora, “RadFormer: Transformers with Global–local Attention for Interpretable and Accurate Gallbladder Cancer detection,” Medical Image Analysis, vol. 83, p. 102676, Jan. 2023.
P. Gupta et al., “Deep-learning Enabled Ultrasound Based Detection of Gallbladder Cancer in Northern India: A Prospective Diagnostic Study,” The Lancet Regional Health - Southeast Asia, Sep. 2023.
P. Gupta et al., “Deep-learning Models for Differentiation of Xanthogranulomatous Cholecystitis and Gallbladder Cancer on Ultrasound,” Indian J Gastroenterol, Dec. 2023.
[9]
[10]
[11]
[12]
[13]
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Thank You
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