EnsembleCAM: Unified Visualization for Explainable Cervical Cancer Identification

Niruthikka Sritharan, Nishaanthini Gnanavel, Prathushan Inparaj, Dulani Meedeniya, Pratheepan Yogarajah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationProceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2024
EditorsChathumi Ayanthi Kavirathna
PublisherIEEE Xplore
Number of pages6
ISBN (Electronic)979-8-3503-7568-8
ISBN (Print)979-8-3503-7569-5
Publication statusPublished (in print/issue) - 11 Jun 2024

Publication series

NameProceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2024

Bibliographical note

Publisher Copyright:
© 2024 IEEE.


  • class activation maps
  • ensemble explanations
  • explainable artificial intelligence
  • medical image classification
  • transparent classification visualization


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