Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique

Faija Islam Oishe; Md. Mejbah Ullah Chowdhury; Muhammad Zawad Mahmud; Samiha Islam; Shahran Rahman Alve

REVIEW 5 major objections 4 minor 1 cited by

Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique

T0 review · 5 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Fine-tuned EfficientNetB3 classifies oral cancer histopathology images with 98.33% accuracy.

desk verdict The 98.33% accuracy claim is unverifiable because the paper never consistently defines the evaluation set; the rest is an incremental benchmark. read the letter →

arxiv 2411.14184 v2 pith:CGIFFS6T submitted 2024-11-21 eess.IV cs.CV

Samiha Islam , Muhammad Zawad Mahmud , Shahran Rahman Alve , Md. Mejbah Ullah Chowdhury , Faija Islam Oishe This is my paper · ORCID

classification eess.IVcs.CV

keywords oralsquamouscellcarcinomaOSCCdeeplearningtransferEfficientNetB3LIMEexplainableAIhistopathologyimageclassification

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a fine-tuned EfficientNetB3 convolutional network can classify histopathology images of oral squamous cell carcinoma (OSCC) against normal tissue with 98.33% accuracy and an F1 score of 0.9844, outperforming ResNet101, DenseNet121, VGG16, and earlier published models on the same public 5,192-image dataset. It also applies LIME, a model-agnostic explanation technique, to show which regions of a slide image push the model toward a diagnosis. The authors present the result as evidence that a relatively small, computationally cheap network can deliver top diagnostic accuracy while providing interpretable output for clinicians. The practical payoff they point to is earlier and more trustworthy detection of oral cancer.

What carries the argument

The load-bearing mechanism is transfer learning with EfficientNetB3, a convolutional network whose compound scaling jointly scales depth, width, and resolution and whose inverted-bottleneck blocks use depthwise separable convolutions and squeeze-and-excitation modules; this is what lets a 12M-parameter model outclass 44.5M-parameter ResNet101 and 138M-parameter VGG16 on this task. The explanation mechanism is LIME (Local Interpretable Model-Agnostic Explanations), which generates 5,000 perturbed pseudo-images around a given test image, weights each pseudo-image by proximity to the original, fits a sparse surrogate model, and returns a superpixel-level saliency mask. That mask is the observable output the paper uses to argue the model is looking at texture- and color-differentiated regions rather than arbitrary artifacts.

What would settle it

Count the images in the actual evaluation run: the reported precision, recall, and accuracy imply 766 correct out of 779, but the methodology section describes a test set of only 31 normal and 95 OSCC images. Re-running EfficientNetB3 on a precisely defined stratified hold-out from the original 5,192 images and comparing the resulting confusion matrix with the reported one would settle whether the 98.33% figure is attached to the dataset the paper claims to test.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that transfer-learning fine-tuning of EfficientNetB3 yields the best published accuracy on the public OSCC histopathology dataset: 98.33% accuracy, 0.9903 precision, 0.9782 recall, and 0.9844 F1-score with 12 million parameters. The confusion matrix reports 356 correct normal and 410 correct OSCC classifications with 13 misclassifications, a total of 779 evaluated images. DenseNet121 is the second-best model at 90.24% accuracy, while the larger ResNet101 and VGG16 models score 89.31% and 81.77%, respectively. LIME visualizations on two example images then attribute the model's decisions to specific superpixel regions, with the authors concluding that texture and color in those regions are decisive for distinguishing infected from normal tissue.

Load-bearing premise

The claim stands or falls with the evaluation set: the accuracy can only be what is reported if the 779-image confusion-matrix set is a fixed, correctly described hold-out with accurate labels, but the paper never consistently states that set—Section III.A describes a 126-image test, mentions a 70:30 split, and the abstract describes a near-balanced 50:50 set—so if the actual test images were chosen after inspecting validation performance or overlap training data, the headline number collapses.

Editorial extensions

If this is right

If the accuracy transfers to unseen clinical slides, OSCC screening could deploy on modest hardware, since EfficientNetB3 reaches this result with 12M parameters while the larger ResNet101 and VGG16 models perform worse.
The LIME saliency maps provide a reviewable output: a pathologist can inspect whether the highlighted superpixels correspond to tumor-associated texture and color before accepting an automated diagnosis.
The comparison table puts fine-tuned EfficientNetB3 ahead of every prior model on the same dataset, so any future work on this benchmark will need to beat 98.33% rather than the previous 97.5%.
The authors' stated next step is to transfer the same fine-tuning-plus-LIME workflow to other cancer types and locally collected hospital images, which would test whether the accuracy is dataset-specific.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A reader who wants to build on this number should first demand a single test-set definition: the paper reports 779 evaluated images in the confusion matrix while describing a 126-image test set elsewhere, so the reproducibility of the headline depends on resolving that discrepancy, not on the model choice.
LIME attribution is a statement about which superpixels move the model's prediction, not about causal tissue biology; comparing the highlighted regions with a pathologist's tumor annotations across many cases would be the natural test of clinical value.
Because training and validation curves show the best checkpoint near epoch 74 and validation accuracy near 98%, the reported test accuracy likely sits close to the validation ceiling; a multi-fold cross-validation would show whether 98.33% is stable or a favorable single split.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The headline number in this paper—98.33% accuracy for EfficientNetB3 on oral SCC histopathology—cannot be verified from the information given. The paper describes the evaluation set three different ways: Section III.A says a 70:30 split and a test set of 31 normal and 95 OSCC images; the abstract suggests a balanced 50:50 split; and the confusion matrix sums to 779 images. 766/779 is exactly 0.9833, so the reported accuracy appears to come from that 779-image set, but no described split of the 5192-image dataset produces 779 test images. The training counts (2435 + 2511 = 4946) plus the stated 126 test images come to 5072, not 5192. This is a load-bearing inconsistency, and the reader's stress-test finding holds up on reading the paper.

The paper isn't without merit. It uses a public dataset, compares four standard backbones, reports confusion matrices and learning curves, and applies LIME to two example images. The related-work survey is reasonably complete, including the Ahmad et al. hybrid model that reached 97% on the same data. The LIME visualizations are illustrative only, and the claim that they explain the model's decisions is overstated—two images are not evidence of explainability.

The soft spots beyond the split problem: no code or hyperparameters are provided, so the work is not reproducible. The best epoch (74) and best model were selected from validation performance, which adds selection bias; if the 779-image set is actually the validation set, the 98.33% is a validation accuracy, not a test result. That distinction is never made clear. There's also a mismatch between the validation accuracy in Table I (97.59%) and the 98.33% in Table II, which suggests the two tables come from different evaluation runs.

Who is this for? Perhaps readers tracking benchmarks on this specific Kaggle dataset, or a class on why evaluation set definition matters. For clinical use, no. For reproducibility, no.

Recommendation: I would not send this to peer review as submitted. The central claim is unverifiable from the paper itself. If the authors provide code, a clearly defined hold-out split, and the actual evaluation-set composition, the result could be checked; as is, reject.

I agree with the reader's REJECT verdict. The paper shows honest effort but not clear enough thinking about the evaluation protocol to be saved by a light revision.

Referee Report

5 major / 4 minor

Summary. The manuscript reports fine-tuning four ImageNet-pretrained CNNs (ResNet101, VGG16, DenseNet121, EfficientNetB3) on the public Histopathological Imaging Database for oral cancer analysis, claiming that EfficientNetB3 reaches 98.33% accuracy and an F1 score of 0.9844 on OSCC versus normal histopathology images, and that LIME visualizations explain the model's decisions. The paper includes training/validation curves, confusion matrices for two models, a metrics table, and a comparison with prior studies. The central claims are that EfficientNetB3 outperforms the other three architectures as well as previously published models on the same dataset, and that LIME adds clinically relevant interpretability.

Significance. If the reported results were reproducible and the evaluation set were correctly specified, the contribution would be an incremental benchmark improvement on a public OSCC histopathology dataset, accompanied by illustrative LIME explanations. The use of a public dataset and the comparison of four standard architectures are appropriate, and the LIME examples are a useful qualitative demonstration. However, the paper's central quantitative claim is not verifiable from the manuscript: the evaluation set is described inconsistently, no code, hyperparameters, or split indices are provided, and the confusion matrix that produces the headline accuracy corresponds to a test set whose origin, size, and class balance are never consistently stated. The significance of the empirical claim is therefore substantially diminished, and the LIME analysis, while visually informative, is limited to two examples without any quantitative validation.

major comments (5)

[III.A and IV.A] The identity of the evaluation set is internally inconsistent, and this inconsistency is load-bearing because the headline accuracy is computed on it. Section III.A states that the testing data contains 31 Normal and 95 OSCC images (126 total) and that the data was split 70:30. The abstract describes a nearly balanced 50:50 split. However, the EfficientNetB3 confusion matrix in Section IV.A sums to 779 images, and 766/779 = 0.9833 exactly equals the reported accuracy. No described split of the 5192-image dataset yields 779 test images: a 70:30 split would give roughly 1558, a 50:50 split roughly 2596, and a 15% split roughly 779, a ratio never mentioned. The manuscript must state which subset produced Fig. 4, how its size and class balance were determined, and why this subset is not the one described in Section III.A.
[III.A and IV.A] The dataset inventory is internally inconsistent, which further undermines the evaluation. The text says the dataset contains 5192 images in total, with 2435 Normal and 2511 OSCC training images. These training counts sum to 4946. Adding the stated 126 test images gives 5072, not 5192. Moreover, the abstract claims a near-even split while Section III.A claims a 70:30 split. The discrepancy of 120 images is never explained. Without a consistent description of how the 5192 images were partitioned into training, validation, and test sets, no reported accuracy can be checked.
[IV.C, Table II, and Fig. 4] The claimed test accuracy appears to be selected from validation performance rather than an independent hold-out. Section IV.A refers to epoch 74 as the 'best epoch' and Section IV.C reports the best validation accuracy as 97.59%, while Table II reports 98.33% accuracy. The paper does not explain how the 98.33% figure follows from the 97.59% validation accuracy, nor whether the confusion matrix in Fig. 4 comes from the validation set, the test set, or a combination. If the 779-image set is the validation set used for early stopping, the reported accuracy would be inflated by model selection and would not be a valid estimate of generalization. The authors must clarify the relationship among Fig. 2, Fig. 4, Table I, and Table II, and report performance on a pre-specified, untouched test set.
[IV.D, Table III] The claim that EfficientNetB3 'outplayed all existing models' is not supported by the comparison table. Table III compares models trained and evaluated on different datasets, different image modalities, and different class balances; for example, [4] reports an F1 score of 98.00 on oral photographic images, not on the same histopathology dataset. A fair comparison would require either the same public dataset with identical splits or a clearly stated cross-dataset benchmark with matched evaluation protocols. As written, Table III cannot substantiate the superiority claim.
[IV.E] The LIME analysis is presented as supporting the explainability and trustworthiness of the model, but the evidence is limited to two randomly selected images, one OSCC and one normal. No quantitative analysis (e.g., overlap of LIME masks with pathologist annotations, stability of explanations across perturbations, or consistency across multiple samples) is provided. The interpretability claim is therefore anecdotal and should be either substantially expanded or explicitly presented as illustrative only.

minor comments (4)

[Abstract and III.A] The abstract's description of the data splitting is grammatically unclear and self-contradictory: it mentions an 'estimated ratio repartition of about 52% for the OSCC group' and then refers to a 'combination set' and 'divided into half,' while Section III.A states a 70:30 split. The split ratio should be stated precisely and consistently in both places.
[II] The related work paragraph for [10] says 'The dataset was the one that we used in this study' but does not identify the dataset by name; for reproducibility, the dataset name and split should be stated explicitly when comparing with prior work.
[III.B and III.C] The preprocessing and training descriptions omit several details needed for reproducibility: the optimizer, learning rate, batch size, number of epochs, augmentation parameters, and the exact fine-tuning strategy (which layers were frozen or trainable) are not reported. No code repository or random seed is provided.
[Throughout] There are numerous typographical errors and inconsistencies, including 'transfer-earning' (Section I), 'EffiecientNetB3' (contributions), 'VGG1616' (Table II), and inconsistent naming of EfficientNetB3 versus EfficientnetB3. These should be corrected in a revised manuscript.

Circularity Check

0 steps flagged · score 2.0 of 10

No derivation-chain circularity; the benchmark is an empirical count. Minor self-citation for LIME and an undefined evaluation set are the only concerns.

full rationale

The paper contains no derivation that reduces an output to an input by definition. The 98.33% accuracy is a count from the EfficientNetB3 confusion matrix (356+410=766 correct out of 779; 766/779=0.9833), and the F1 of 0.9844 follows from the same counts; these are empirical measurements, not fitted parameters renamed as predictions. The one self-citation, reference [14], is used in Section III.D to support the description of LIME's surrogate-sample weighting; LIME is a standard external method, so this citation is minor and not load-bearing. The paper does, however, have a substantial non-circular reproducibility flaw: Section III.A describes a test set of 31 Normal and 95 OSCC images (126 total) under a 70:30 split, while the confusion matrix in Section IV.A totals 779 images and exactly reproduces the reported accuracy; the abstract refers to a separate near-50:50 'combination set.' Section IV.A also selects the best epoch from the validation curves. Because the paper never reconciles these descriptions, the evaluation set behind the headline benchmark is undefined. That undermines verifiability and independence of the performance claim, but it is not a circular reduction, so the circularity score remains at 2.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim is an empirical accuracy number, so the ledger is dominated by selection choices (architecture, epoch, split) rather than derived constants. No new physical or conceptual entities are introduced. The split inconsistency is the most important uncharged input.

free parameters (3)

best architecture selection = EfficientNetB3
Chosen as best of four pretrained CNNs based on validation metrics; the central accuracy claim is conditional on this post hoc selection.
best epoch = 74
The stopping point is selected from the validation loss and accuracy curves; reporting accuracy at this epoch rather than a pre-specified rule can inflate the headline number.
evaluation split = 779-image set (implied by confusion matrix)
The evaluation set size and balance vary across the paper; the headline 98.33% matches 766 out of 779 correct, so the central result depends on an undefined hold-out split.

assumptions (3)

domain assumption The Kaggle histopathology dataset labels (Normal versus OSCC) are correct and the same under the stated 70:30 and balanced splits.
The entire accuracy claim assumes ground-truth labels in the public dataset are accurate; the paper does not validate them against a pathologist's re-review. Section III.A.
domain assumption The chosen hold-out set is independent of training and contains no duplicate or near-duplicate images across splits.
The paper's confusion matrix and accuracy reflect a 779-image set whose relation to the stated 31/95 test set is unexplained; if overlap or leakage exists, the claim fails. Sections III.A and IV.A.
domain assumption Pretrained ImageNet weights transfer usefully to histopathology images for this task.
All four models are initialized from ImageNet pretraining and fine-tuned; performance is reported conditional on this transfer being appropriate. Section III.C.

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Cite this review

Pith. "Pith review of Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique." pith.science (2026). https://pith.science/paper/CGIFFS6T

@misc{pith2026241114184,
  author       = {Pith},
  title        = {Pith review of: Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CGIFFS6T}},
  note         = {Machine review of arXiv:2411.14184}
}

read the original abstract

The goal of the present study is to analyze an application of deep learning models in order to augment the diagnostic performance of oral squamous cell carcinoma (OSCC) with a longitudinal cohort study using the Histopathological Imaging Database for oral cancer analysis. The dataset consisted of 5192 images (2435 Normal and 2511 OSCC), which were allocated between training, testing, and validation sets with an estimated ratio repartition of about 52% for the OSCC group, and still, our performance measure was validated on a combination set that contains almost equal number of sample in this use case as entire database have been divided into half using stratified splitting technique based again near binary proportion but total distribution was around even. We selected four deep-learning architectures for evaluation in the present study: ResNet101, DenseNet121, VGG16, and EfficientnetB3. EfficientNetB3 was found to be the best, with an accuracy of 98.33% and F1 score (0.9844), and it took remarkably less computing power in comparison with other models. The subsequent one was DenseNet121, with 90.24% accuracy and an F1 score of 90.45%. Moreover, we employed the Local Interpretable Model-agnostic Explanations (LIME) method to clarify why EfficientNetB3 made certain decisions with its predictions to improve the explainability and trustworthiness of results. This work provides evidence for the possible superior diagnosis in OSCC activated from the EfficientNetB3 model with the explanation of AI techniques such as LIME and paves an important groundwork to build on towards clinical usage.

Figures

Figures reproduced from arXiv: 2411.14184 by the authors.

**Figure 3.** Training and validation loss curve of EfficientNetB3 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** visualizes the confusion matrix of the EfficientNetB3 model. It is seen that the model classified 356 images correctly as normal, labeled as 0. 410 images were classified correctly as infected, labeled as 1. Only 13 images were classified wrongly. It shows the model’s robustness in classifying the disease. B. DenseNet121 [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

Figures from the paper (5 more)

**Figure 5.** Figure 5: Training and validation accuracy curve of DenseNet121 [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Train and validation loss curve of DenseNet121 [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

**Figure 7.** Figure 7: Confusion Matrix of DenseNet121 C. Model Evaluation Table I evaluates the applied models in terms of training accuracy, validation accuracy, and validation loss. It is apparent from the table that the EfficientNetB3 model worked better than all other applied models in…

**Figure 8.** Figure 8: LIME XAI visualization for the EfficientNetB3 model on an infected [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

**Figure 9.** Figure 9: LIME XAI visualization for the EfficientNetB3 model on a non [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [4]

AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer,

K. Warin, W. Limprasert, S. Suebnukarn, S. Jinaporntham, P. Jantana, and S. Vicharueang, “AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer,” Plos one, vol. 17, no. 8, p. e0273508, 2022

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D. K. Redie, S. Bilgaiyan, and S. Sagnika, “Oral cancer detection using transfer learning-based framework from histopathology images,” Journal of Electronic Imaging , vol. 32, no. 5, pp. 053 004–053 004, 2023

work page 2023

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Intelligent deep learning enabled oral squamous cell carcinoma detection and classification using biomedical images,

A. A. Alanazi, M. M. Khayyat, M. M. Khayyat, B. M. Elamin Elnaim, and S. Abdel-Khalek, “Intelligent deep learning enabled oral squamous cell carcinoma detection and classification using biomedical images,” Computational Intelligence and Neuroscience , vol. 2022, no. 1, p. 7643967, 2022

work page 2022

[4] [3]

Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network,

N. Das, E. Hussain, and L. B. Mahanta, “Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network,” Neural Networks, vol. 128, pp. 47–60, 2020

work page 2020

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Capsule network based analysis of histopathological images of oral squamous cell carcinoma,

S. Panigrahi, J. Das, and T. Swarnkar, “Capsule network based analysis of histopathological images of oral squamous cell carcinoma,” Journal of King Saud University-Computer and Information Sciences , vol. 34, no. 7, pp. 4546–4553, 2022

work page 2022

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Early diagnosis of oral cancer using image processing and artificial intelligence,

E. S. Mira, A. M. S. Sapri, R. F. Aljehanı, B. S. Jambı, T. Bashir, E.-S. M. El-Kenawy, M. Saber et al. , “Early diagnosis of oral cancer using image processing and artificial intelligence,” Fusion: Practice and Applications, vol. 14, no. 1, pp. 293–308, 2024

work page 2024

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Current insights into oral cancer diagnostics,

Y .-F. Su, Y .-J. Chen, F.-T. Tsai, W.-C. Li, M.-L. Hsu, D.-H. Wang, and C.-C. Yang, “Current insights into oral cancer diagnostics,” Diagnostics, vol. 11, no. 7, p. 1287, 2021

work page 2021

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Automated detection and classification of oral lesions using deep learning for early detection of oral cancer,

R. A. Welikala, P. Remagnino, J. H. Lim, C. S. Chan, S. Rajendran, T. G. Kallarakkal, R. B. Zain, R. D. Jayasinghe, J. Rimal, A. R. Kerr et al., “Automated detection and classification of oral lesions using deep learning for early detection of oral cancer,” Ieee Access , vol. 8, pp. 132 677–132 693, 2020

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