Abstract
Cervical cancer, which is ranked fourth among cancers affecting women, is highly treatable when detected early through the pap smear test. Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), analyze pap smear images, yet their Black-Box nature raises transparency concerns in medical diagnostics. This paper introduces a solution named EnsembleCAM to enhance interpretability by unifying visual explanations through the combination of diverse Class Activation Maps (CAMs). Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques, develop an XceptionNet based binary classification model with an accuracy of 89% and apply GradCAM, GradCAM++, Score-CAM, Eigen-CAM and LayerCAM on this classifier. Then, the novel EnsembleCAM is constructed taking the median of activation maps from the five individual CAM methods. The analysis of activation maps of each CAM method and EnsembleCAM confirmed that in activation maps of EnsembleCAM, higher activation values were more concentrated around the nucleus which is the most important region in indicating cervical malignancy. The evaluation using pixel flipping performance metric also proved that the EnsembleCAM effectively recognises regions vital to the model's decision-making through its steepest drop in the mean prediction score when the pixels in the region contributing most to the model's decision were flipped.
Original language | English |
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Title of host publication | Proceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2024 |
Editors | Chathumi Ayanthi Kavirathna |
Publisher | IEEE Xplore |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 979-8-3503-7568-8 |
ISBN (Print) | 979-8-3503-7569-5 |
DOIs | |
Publication status | Published (in print/issue) - 11 Jun 2024 |
Publication series
Name | Proceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2024 |
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Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- class activation maps
- ensemble explanations
- explainable artificial intelligence
- medical image classification
- transparent classification visualization
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SCSE 2024 - The Best Paper Award of the Smart Computing Track
Yogarajah, P. (Recipient), 4 Apr 2024
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