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REVIEW 5 major objections 6 minor 42 references

Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A DenseNet201-encoded U-Net tuned by spider monkey optimization reaches 96.16% accuracy and 91.63% IoU on Pap smear segmentation.

desk verdict A plain encoder-swap plus metaheuristic tuning, but the headline accuracy gain is unsupported because the comparison lacks a controlled baseline and a defined validation protocol. read the letter →

arxiv 2504.12807 v1 pith:VK7KOFSE submitted 2025-04-17 cs.CV cs.AI

Ach Khozaimi , Isnani Darti , Syaiful Anam , Wuryansari Muharini Kusumawinahyu This is my paper · ORCID
classification cs.CVcs.AI
keywords PapsmearcervicalcancersemanticsegmentationDenseNet201U-NetSpiderMonkeyOptimizationSIPaKMeDCLAHE
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 proposes combining a DenseNet201 encoder with a U-Net decoder and using the spider monkey optimization (SMO) algorithm to choose the learning rate, batch size, and number of epochs, after also using SMO to tune a Perona-Malik diffusion plus CLAHE preprocessing step. The authors claim that, on the SIPaKMeD cervical cytology dataset, the optimized model segments cell images with 96.16% accuracy, 91.63% IoU, and 95.63% Dice coefficient. They further claim this beats standard U-Net, Res-UNet50, and Efficient-UNetB0 under the same preprocessing, and that the SMO tuning step accounts for a large final jump in performance. The work positions pretrained encoders, SMO-tuned image enhancement, and metaheuristic hyperparameter search as complementary routes to better cervical cell segmentation.

What carries the argument

The central mechanism is Dense-UNet201 plus a modified spider monkey optimizer. DenseNet201's dense blocks concatenate features from all preceding layers in the encoder, which the paper argues improves gradient flow and feature reuse, while the U-Net decoder restores spatial detail through transpose convolutions aligned with encoder skip connections and a bottleneck that stays at 16×16×1024. The SMO component encodes each candidate solution as a triple of learning rate, batch size, and epoch count; continuous updates act on the learning rate, discrete updates round the epoch count to stay in [10,100], and categorical updates move an index mapped to the batch-size set. The swarm objective is the segmentation loss, and the optimizer's search is what the paper credits for the final performance gain.

What would settle it

Train Dense-UNet201 without SMO using exactly the learning rate, batch size, and epoch count that SMO selected, on the same SMO-PMD-CLAHE enhanced data and the same train/validation/test split; if it also reaches roughly 96% accuracy and 91% IoU, the SMO-specific claim is falsified. A supporting check is to confirm the SMO hyperparameters were chosen on a validation set and to report results from multiple random seeds.

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Extended reading notes

Core claim

On the SIPaKMeD cervical cytology dataset, the paper claims that a segmentation network built by replacing the U-Net encoder with an ImageNet-pretrained DenseNet201, trained on images enhanced by an SMO-tuned hybrid Perona-Malik diffusion and CLAHE filter, and with learning rate, batch size, and epoch count selected by a modified spider monkey optimization algorithm, reaches 96.16% accuracy, 91.63% IoU, and 95.63% Dice coefficient. The SMO modifications round discrete epoch values and map categorical batch sizes to indices so the swarm search can handle mixed-variable hyperparameters. The paper further claims that these scores exceed those of standard U-Net, Res-UNet50, and Efficient-UNetB0 under the same preprocessing, and that the SMO step raises Dense-UNet201 from 92.02% accuracy, 80.76% IoU, and 88.18% Dice to the final numbers while reducing loss from 13.28% to 5.03%.

Load-bearing premise

The comparison assumes that the non-SMO Dense-UNet201 baseline was trained with reasonable, comparable hyperparameters, so the large gain in the third scenario is caused by SMO rather than by starting from a poorly tuned baseline.

Editorial extensions

If this is right

  • If the reported numbers reproduce, Dense-UNet201 becomes a strong baseline for cervical cell segmentation on SIPaKMeD, with a pretrained encoder adding roughly 22 percentage points of IoU over standard U-Net in the raw-data scenario.
  • The mixed-variable SMO encoding gives a template for applying swarm optimizers to deep learning hyperparameters that include categorical choices like batch size, not just continuous learning rates.
  • The SMO-tuned PMD-CLAHE preprocessing step improves every compared architecture, so the paper positions image enhancement and segmentation as jointly optimizable components.
  • The SMO tuning step is claimed to be responsible for the largest single gain, raising IoU by 10.87 percentage points and Dice by 7.45 percentage points over the preprocessed but untuned model.

Reading between the lines

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

  • A natural extension is to use the SMO-selected hyperparameters as a fixed configuration and retrain Dense-UNet201 without the swarm loop, which would isolate how much of the gain comes from the search itself versus from the discovered hyperparameter values.
  • The same mixed-variable encoding could be applied to other architectural choices, such as decoder depth, loss weights, or augmentation settings, turning SMO into a general network-configuration search rather than a three-parameter tuner.
  • Because the preprocessing step improves all compared models, a promising follow-up is to test whether SMO-tuned PMD-CLAHE also helps other cervical cytology datasets and other stain-normalization pipelines.
  • The reported single-trial improvements would be more convincing with multiple seeds and confidence intervals, since deep learning runs can vary by several IoU points across seeds.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper proposes Dense-UNet201, a U-Net variant with a pretrained DenseNet201 encoder, for semantic segmentation of Pap smear images from the SIPaKMeD dataset. The authors combine this architecture with spider monkey optimization (SMO) for hyperparameter tuning and with an SMO-optimized PMD-CLAHE preprocessing step. Three scenarios are compared: no preprocessing, preprocessing only, and preprocessing plus SMO optimization of Dense-UNet201. The central claim is that SMO raises Dense-UNet201 from 92.02% accuracy, 80.76% IoU, and 88.18% Dice in the second scenario to 96.16% accuracy, 91.63% IoU, and 95.63% Dice in the third scenario, and that this improvement is attributable to SMO.

Significance. If the central claim were established, the paper would provide a useful case study of metaheuristic hyperparameter optimization for medical image segmentation and a systematic comparison of pretrained encoders in U-Net. The manuscript has strengths: it uses a public benchmark dataset, reports standard segmentation metrics, compares four architectures under the same preprocessing, and gives a reasonably detailed description of the SMO modifications for categorical and discrete variables. However, the main causal claim that SMO is responsible for the large performance gains is not supported by the experimental design as reported. The lack of a defined data split, the absence of baseline hyperparameters, and the single-run evaluation mean that the reported improvement could plausibly be due to selection bias, undertrained baselines, or random variation.

major comments (5)
  1. [§2.6–2.7, §3.3] The manuscript never defines a training/validation/test split for the SIPaKMeD images, even though §2.7 states that metrics are reported on both validation and test data. Without a specified split and a rule that SMO hyperparameter selection uses only the validation set, the third-scenario results may have been selected directly on the test set, which would inflate the reported accuracy (96.16%), IoU (91.63%), and Dice (95.63%). Please specify the split, the number of SMO fitness evaluations, and which data were used at each stage of the optimization.
  2. [§3.2 vs. §3.3] The comparison that supports the central claim is uncontrolled. For the Scenario-2 baseline Dense-UNet201, the paper does not report the learning rate, batch size, or number of epochs; §2.2 only states that Adamax is used for all models. If the baseline was trained with different or poorly chosen hyperparameters, the SMO improvement reported in Figure 7 could reflect under-training rather than the optimization algorithm. The baseline and SMO-tuned runs must use the same data split, training budget, initialization, and evaluation protocol.
  3. [Figure 7, §3.3] All results are single-run point estimates without error bars, confidence intervals, or significance tests. Given the large reported jumps in IoU and Dice, repeated runs with different random seeds are needed to establish that the differences are not due to random variation. At minimum, the authors should report the number of runs and the variance across runs.
  4. [§2.2] The learning-rate search range is stated inconsistently within the same section: the text first says the range is 1e-5 to 1e-2 and later says 10^-5 to 10^-1. This factor-of-ten discrepancy changes the search space and prevents reproduction of the SMO optimization. Please correct the inconsistency and state the exact range used.
  5. [§2.5, Eq. (9)] The probability update for categorical parameters in Eq. (9) is not clearly defined: the numerator uses fitness(LL_k) while the denominator sums fitness over all monkeys, and the relationship between the index update in Eq. (8) and this probability is unexplained. This makes the modified SMO difficult to reproduce and should be clarified.
minor comments (6)
  1. [Figures] Figure numbering is inconsistent: §2.3 says 'Fig. 3 illustrates the adjusted DenseNet-201 architecture' but Figure 3 is the U-Net diagram, and §2.6 also refers to Figure 2 for the simulation scenario, duplicating an earlier Figure 2. All figures should be renumbered and cross-checked.
  2. [§2.4] The U-Net description gives an input size of 572×572×1, whereas the experimental protocol in §2.1 resizes all images to 256×256. Please clarify which input size was actually used.
  3. [§2.7, Eqs. (11)–(12)] The notation in Eqs. (11) and (12) defines P as the ground-truth object and G as the predicted object, which is the reverse of the conventional assignment; please align the notation with the text or with standard usage.
  4. [References] Reference [35] is cited as the source of the SMO-optimized PMD-CLAHE preprocessing but appears to be a preprint by the same authors; please provide a persistent identifier and enough detail in this paper to make the preprocessing reproducible without relying on that reference.
  5. [Throughout] There are several typographical errors, including 'bith size' in §2.5, 'DenseNe201' in §1, and inconsistent notation for powers of ten (10^-5 vs. 1e-5); these should be corrected in a final pass.
  6. [§2.7, Eq. (13)] The dice-loss expression in Eq. (13) appears to contain a typo: the second summation uses C_c where G_c is expected. Please verify the formula against the cited source.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported SMO Dense-UNet201 results are empirical evaluations on the public SIPaKMeD dataset, and the claimed SMO benefit is not an input renamed as a prediction.

full rationale

The paper's central claim is an empirical segmentation result: SMO Dense-UNet201 reaches 96.16% accuracy, 91.63% IoU, and 95.63% Dice on SIPaKMeD. This is not a derivation from an input but a measured outcome on a public dataset with ground-truth masks, so it is externally falsifiable and not equivalent to any fitted parameter by construction. Scenario 2 versus Scenario 3 compares Dense-UNet201 with and without SMO hyperparameter tuning under the same SMO PMD-CLAHE preprocessing, so the claimed improvement is not logically entailed by the self-cited preprocessing work [35]. The self-citation [35] supplies the preprocessing recipe, but the paper itself observes the preprocessing benefit empirically in Section 3.2, and the central SMO-versus-baseline comparison holds preprocessing fixed, so the self-citation is not load-bearing in the strong sense that would constitute circularity. The manuscript omits train/validation/test split details and baseline hyperparameters (Section 2.7 says metrics are reported 'on validation and test data' without defining the split; Section 2.2 gives only Adamax as the optimizer), and the SMO fitness function is not defined (Eq. 3 uses 'fitness_i' without specifying it), so whether the final metrics were selection criteria rather than independent test-set numbers cannot be verified from the text. These are methodological transparency and correctness-risk concerns, not circularity: no equation in the paper defines the reported metric as the SMO objective, and no fitted value is renamed as a prediction. The learning-rate range inconsistency (1e-5 to 1e-2 in Section 2.2 versus 10^-5 to 10^-1 later) is likewise an internal inconsistency, not a circular step.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper's central result rests on several unstated choices: the exact hyperparameters of the baseline models, the SMO search budget, the data splitting procedure, and the parameters of the self-cited PMD-CLAHE preprocessing. None of these are disclosed, so the reported performance numbers cannot be independently reproduced or causally attributed.

free parameters (4)
  • SMO swarm size
    Not reported. The number of candidate hyperparameter configurations evaluated by SMO is essential to interpreting the optimization cost and search quality.
  • SMO iteration count and leader limits
    Not reported. These control when the algorithm terminates and how aggressively it explores, but the paper only describes the general SMO phases.
  • Final optimized hyperparameters (learning rate, batch size, epochs)
    The selected values are never listed, making the optimized configuration irreproducible.
  • PMD-CLAHE filter parameters
    The preprocessing is claimed to be SMO-optimized, but the filter parameters are deferred to the authors' own companion paper [35].
assumptions (4)
  • domain assumption SIPaKMeD ground-truth masks are accurate.
    The authors reconstruct masks from the dataset's *.cyt*.dat annotation files after resizing to 256x256 JPG, and assume label fidelity survives this conversion (Section 2.1).
  • domain assumption ImageNet pretraining transfers to Pap smear cell segmentation.
    The DenseNet201 encoder is initialized with ImageNet weights; the paper cites DenseNet's classification success but gives no evidence for segmentation transfer (Section 2.2).
  • ad hoc to paper SMO converges to a good hyperparameter set.
    The convergence description in Section 2.5 is qualitative and no comparison to grids, random search, or Bayesian optimization is provided.
  • domain assumption Adamax is a valid fixed optimizer for all baseline models.
    The paper states 'Adamax was used for all the models' (Section 2.2) but gives no comparison with other optimizers.

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Cite this review

Pith. "Pith review of Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization." pith.science (2026). https://pith.science/paper/VK7KOFSE

@misc{pith2026250412807,
  author       = {Pith},
  title        = {Pith review of: Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VK7KOFSE}},
  note         = {Machine review of arXiv:2504.12807}
}
read the original abstract

Pap smear image segmentation is crucial for cervical cancer diagnosis. However, traditional segmentation models often struggle with complex cellular structures and variations in pap smear images. This study proposes a hybrid Dense-UNet201 optimization approach that integrates a pretrained DenseNet201 as the encoder for the U-Net architecture and optimizes it using the spider monkey optimization (SMO) algorithm. The Dense-UNet201 model excelled at feature extraction. The SMO was modified to handle categorical and discrete parameters. The SIPaKMeD dataset was used in this study and evaluated using key performance metrics, including loss, accuracy, Intersection over Union (IoU), and Dice coefficient. The experimental results showed that Dense-UNet201 outperformed U-Net, Res-UNet50, and Efficient-UNetB0. SMO Dense-UNet201 achieved a segmentation accuracy of 96.16%, an IoU of 91.63%, and a Dice coefficient score of 95.63%. These findings underscore the effectiveness of image preprocessing, pretrained models, and metaheuristic optimization in improving medical image analysis and provide new insights into cervical cell segmentation methods.

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Reference graph

Works this paper leans on

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    INTRODUCTION Cancer is the second leading cause of death worldwide, after heart disease [1]. Cervical cancer is a major health concern for women worldwide. Cervical cancer cells can spread to other organs [2]. The Global Cancer Observatory (GCO) reported 570,000 new cases and 311,000 deaths due to cervical cancer in 2018 [3]. In Indonesia, it is the secon...

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    MATERIALS AND METHODS 2.1. Pap Smear Images, Preprocessing, and Annotation Process The SIPaKMeD dataset was used to evaluate SMO Dense-UNet201. The SIPaKMeD dataset is well- structured for cervical cell analysis, including medical image classification and semantic image segmentation. It contains 4,049 isolated cervical cell images categorized into five di...

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    RESULTS AND DISCUSSION This study evaluated the Hybrid Dense-UNet201 method in three scenarios to assess its performance in multi-class semantic segmentation. Simulations were conducted using the SIPaKMeD dataset. The performance of the Hybrid Dense -UNet201 model was compared with that of three semantic segmentation models: U-Net, Res-UNet50, and Efficie...

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    CONCLUSION This study evaluated the Hybrid Dense -UNet201 model in three scenarios for multi -class semantic segmentation of Pap smear images. Results showed that Hybrid Dense-UNet201 consistently outperformed U- Net, Res-UNet50, and Efficient -UNetB0 in accuracy, IoU, a nd Dice coefficient. In the first scenario, Hybrid Dense-UNet201 achieved an accuracy...

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    The final layer converts the features into an output segmentation map

    Each upsampling stage is followed by a 3 × 3 convolution with ReLU to refine the extracted features. The final layer converts the features into an output segmentation map. With this structure, U-Net is highly effective for medical image segmentation. Fig. 4 shows the U-Net arc...

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Reviewed August 16, 2026 · model on record in the stance chip above.