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A Comparative Analysis of Explainable AI (XAI) Techniques for Transparent and Reliable Image Classification.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § 3. Methodology › 3.3. Test Models: Role of CNN Architecture Complexity ↔ xai_c/models/definitions.py, lines 5–36 · score 0.73 · AvgPool2d, ReLU, kernel, module, stride, padding
  2. [2] § 3. Methodology › 3.2. Preprocessing of Dataset ↔ CNN_XAI_Analysis.py, lines 190–201 · score 0.60 · ToTensor, PIL image, resize, model

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 77 lines · 2.2 KB · Apache-2.0 · 1 match

  1. import torch
  2. import torch.nn as nn
  3. import torchvision.models as models
  5. # --- CNN Definition ---
  6. class CNN(nn.Module):
  7. def __init__(self):
  8. super(CNN, self).__init__()
  9. self.features = nn.Sequential(
  10. nn.Conv2d(3, 8, kernel_size=3, stride=1, padding=1),
  11. nn.ReLU(inplace=True),
  12. nn.AvgPool2d(kernel_size=2, stride=2),
  14. nn.Conv2d(8, 16, kernel_size=3, stride=1, padding=1),
  15. nn.ReLU(inplace=True),
  16. nn.AvgPool2d(kernel_size=2, stride=2),
  18. nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
  19. nn.ReLU(inplace=True),
  20. nn.AvgPool2d(kernel_size=2, stride=2),
  22. nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
  23. nn.ReLU(inplace=True),
  24. nn.AvgPool2d(kernel_size=2, stride=2)
  25. )
  26. self.classifier = nn.Sequential(
  27. nn.Linear(64 * 14 * 14, 64),
  28. nn.ReLU(inplace=True),
  29. nn.Linear(64, 2)
  30. )
  32. def forward(self, x):
  33. x = self.features(x)
  34. x = x.view(x.size(0), -1) # Flatten
  35. x = self.classifier(x)
  36. return x
  38. # --- Loaders ---
  40. def load_vgg16(weights_path, device):
  41. """
  42. Loads VGG16 with a binary classifier (Sigmoid output).
  43. """
  44. model = models.vgg16()
  45. # Modify classifier for binary output as requested
  46. model.classifier[6] = nn.Sequential(
  47. nn.Linear(4096, 1),
  48. nn.Sigmoid()
  49. )
  51. try:
  52. state_dict = torch.load(weights_path, map_location=device, weights_only=True)
  53. except TypeError:
  54. # Fallback for older pytorch versions
  55. state_dict = torch.load(weights_path, map_location=device)
  57. model.load_state_dict(state_dict)
  58. model.to(device)
  59. model.eval()
  60. return model
  62. def load_cnn(weights_path, device):
  63. """
  64. Loads Custom CNN with split state_dict (features/classifier).
  65. """
  66. model = CNN()
  68. # Load the dictionary of state dicts
  69. state_dict = torch.load(weights_path, map_location=device)
  71. # Load into respective parts
  72. model.features.load_state_dict(state_dict['features'])
  73. model.classifier.load_state_dict(state_dict['classifier'])
  75. model.to(device)
  76. model.eval()
  77. return model

definitions.py at commit a3145d6, under Apache-2.0 · at the source

Overview

Authors: Sovon Chakraborty1, Shakib Mahmud Dipto1, Kevin R. Pilkiewicz2, Michael L. Mayo2, Pratip Rana1
ORCID iDs: Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana
  1. Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA; (S.C.); (S.M.D.)
  2. Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 39180, USA; (K.R.P.); (M.L.M.)
Institutions: Old Dominion University (United States); United States Army (United States); U.S. Army Engineer Research and Development Center (United States)
Journal: Entropy (Basel, Switzerland), volume 28, issue 5, article 562
Dates: received 12 February 2026; accepted 6 May 2026; published online 18 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/e28050562 · PMID 42187977 · PMCID PMC13205289 · OpenAlex W7161676037
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: methods / tools (subfield)
Methods: Connectivity, Machine learning
Keywords: XAI, image classification, LIME, Grad-CAM, PEEK, LRP, interpretability
Topic: Explainable Artificial Intelligence (XAI) (Artificial Intelligence, Computer Science), according to OpenAlex
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and computational efficiency of these methods using a globally recognized dataset. With 7390 images, the Oxford IIT pet dataset provides a comprehensive resource for training a custom Convolutional Neural Network (CNN) and VGG16, enabling a consistent evaluation of each XAI method. First, we analyze the saliency maps of the input images and observe the regions predicted by these XAI methods, and then leverage a noise analysis approach to evaluate their performance in terms of accuracy. We further explore the robustness, run-time, and “faithfulness” metrics of each XAI method. In general, we find that these methods can identify a set of input-data features that are critical for accurate classification but also intuitive, such as the outline, face, and eyes of subjects. However, our analysis reveals only marginal consensus among XAI methods in identifying those critical features. Grad-CAM demonstrates strong robustness and stability in VGG16, but the performance on the shallow CNN model remained inconsistent.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

AMLRL-ODU/CompareXAI

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: a3145d624eac9ca8c9ed3c5001829707e8d3fe3e, 18 May 2026
Languages: Python (20)
Size: 23 files, 20 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (15 files), NumPy (11 files), Matplotlib (8 files), pandas (3 files), seaborn (3 files), OpenCV (2 files), Pillow (2 files), SciPy (2 files), scikit-learn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
22 files
At the source: github.com/AMLRL-ODU/CompareXAI

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 20 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

No new data were created or analyzed in this study. All the codes and data are available in the mentioned Github link: https://github.com/AMLRL-ODU/CompareXAI (accessed on 11 February 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 keywords, 27 references.

Cite

This paper

Chakraborty, S., Dipto, S. M., Pilkiewicz, K. R., Mayo, M. L., & Rana, P. (2026). A Comparative Analysis of Explainable AI (XAI) Techniques for Transparent and Reliable Image Classification. Entropy (Basel, Switzerland), 28(5), 562. https://doi.org/10.3390/e28050562

BibTeX

@article{chakraborty2026comparative,
author = {Chakraborty, Sovon and Dipto, Shakib Mahmud and Pilkiewicz, Kevin R. and Mayo, Michael L. and Rana, Pratip},
title = {{A Comparative Analysis of Explainable AI (XAI) Techniques for Transparent and Reliable Image Classification}},
journal = {Entropy (Basel, Switzerland)},
year = {2026},
month = may,
volume = {28},
number = {5},
pages = {562},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1099-4300},
doi = {10.3390/e28050562},
url = {https://doi.org/10.3390/e28050562},
pmid = {42187977},
pmcid = {PMC13205289}
}

RIS

TY - JOUR
AU - Chakraborty, Sovon
AU - Dipto, Shakib Mahmud
AU - Pilkiewicz, Kevin R.
AU - Mayo, Michael L.
AU - Rana, Pratip
TI - A Comparative Analysis of Explainable AI (XAI) Techniques for Transparent and Reliable Image Classification
T2 - Entropy (Basel, Switzerland)
J2 - Entropy (Basel)
PY - 2026
DA - 2026/05/18
VL - 28
IS - 5
SP - 562
SN - 1099-4300
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/e28050562
UR - https://doi.org/10.3390/e28050562
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "A Comparative Analysis of Explainable AI (XAI) Techniques for Transparent and Reliable Image Classification",
"container-title": "Entropy (Basel, Switzerland)",
"author": [
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"family": "Chakraborty",
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]
}
}

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