The Potential of Convolutional Neural Networks for Cancer Detection

Hossein Molaeian; Kaveh Karamjani; Saeed Roshani; Sina Teimouri; Sobhan Roshani

The Potential of Convolutional Neural Networks for Cancer Detection

T0 review · 1 major / 0 minor · reviewed 2026-05-23 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Convolutional neural networks can complement traditional methods for detecting ten common cancers.

desk verdict This is a standard literature survey on CNNs for cancer detection that doesn't standardize cross-study comparisons, so its rankings of architectures aren't reliable. read the letter →

arxiv 2412.17155 v4 pith:RLHGPONY submitted 2024-12-22 cs.CV cs.LG

Hossein Molaeian , Kaveh Karamjani , Sina Teimouri , Saeed Roshani , Sobhan Roshani This is my paper

classification cs.CVcs.LG

keywords convolutionalneuralnetworkscancerdetectionmedicalimagingdeeplearningclinicalintegrationdiagnosticmethodspatternrecognition

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 review examines applications of convolutional neural networks to image-based classification of ten cancers. It compares the distinct architectures used in each case, identifies their advantages and disadvantages on the available datasets, and concludes that CNNs hold potential for integration into clinical practice alongside existing diagnostic tools. A sympathetic reader would care because improved early detection directly raises treatment success and survival rates for common cancers. The work points to certain top-performing architectures as the most promising for this clinical role.

What carries the argument

Convolutional neural network architectures for pattern recognition in cancer imaging datasets from multiple studies.

What would settle it

A controlled clinical study showing that adding the identified top CNN architectures to standard diagnostic workflows produces no measurable gain in accuracy or patient outcomes would disprove the integration potential.

Watch

Extended reading notes

Core claim

The paper claims that CNNs have the potential to be integrated into clinical practice to complement traditional diagnostic methods, with certain architectures identified as top-performing for enhancing diagnostic capabilities across ten cancers.

Load-bearing premise

The reviewed studies provide a representative sample of CNN applications across ten cancers with comparable datasets and evaluation standards.

Editorial extensions

If this is right

Top-performing CNN architectures could be prioritized when selecting tools to support cancer diagnosis.
Integration would position CNNs as an aid that works together with rather than replaces traditional methods.
Identified advantages and disadvantages would help match specific architectures to particular cancer types.

Reading between the lines

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

The same comparison method could be applied to CNN uses in screening for cancers beyond the ten covered here.
Hospitals might test protocols that feed CNN outputs into existing review processes to speed initial case triage.
Performance on new, unseen clinical datasets would provide a direct next test of the top architectures.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

This paper is a survey that reviews how convolutional neural networks have been applied to detect ten common cancers in published studies. It doesn't introduce any new methods or results of its own. The authors go through existing work, note the different CNN architectures used for each cancer type, and compare their reported advantages and disadvantages. They conclude that these models show promise for clinical use alongside traditional diagnostics and point to some as top performers. The problem is that the comparisons across studies don't appear to account for major differences in the data. Cancers involve varied imaging modalities, dataset sizes, and evaluation methods like accuracy versus AUC or internal versus external validation. So ranking architectures without harmonizing those factors risks crediting study-specific quirks instead of model quality. The abstract doesn't mention any such adjustments or subgroup analyses. Because only the abstract is available, it's impossible to verify the details of how the review was conducted or whether the selected studies form a balanced sample across the ten cancers. This might serve as an entry point for readers new to the topic who want an overview of the literature. It won't offer much to specialists already familiar with medical imaging applications of deep learning. I don't see a reason to discuss it in a reading group or to cite it in my own papers. I would not recommend sending this for peer review. A survey needs to demonstrate careful synthesis and handling of heterogeneity to be worth referee effort, and this one doesn't show that from what's provided.

Referee Report

1 major / 0 minor

Summary. The manuscript is a literature review claiming that CNNs show strong potential for clinical integration in detecting ten cancers. By comparing distinct CNN architectures across studies on different datasets, it identifies advantages/disadvantages of each approach and names top-performing architectures to enhance diagnostic capabilities alongside traditional methods.

Significance. If the review rigorously harmonizes metrics, accounts for dataset heterogeneity, and validates cross-study rankings, it could usefully guide selection of CNNs for cancer detection tasks. The abstract, however, supplies no methodological details supporting such rigor.

major comments (1)

[Abstract] Abstract: the central claim that top-performing CNN architectures can be identified rests on cross-study comparisons, yet the text gives no indication of metric harmonization, subgroup analysis, or explicit controls for confounders such as imaging modality, dataset size, class imbalance, or validation protocol (accuracy vs. AUC, internal vs. external). Without these, any ranking is vulnerable to study-specific artifacts rather than intrinsic superiority.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their comments on our literature review. We address the concern about methodological transparency in the abstract below.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that top-performing CNN architectures can be identified rests on cross-study comparisons, yet the text gives no indication of metric harmonization, subgroup analysis, or explicit controls for confounders such as imaging modality, dataset size, class imbalance, or validation protocol (accuracy vs. AUC, internal vs. external). Without these, any ranking is vulnerable to study-specific artifacts rather than intrinsic superiority.

Authors: We agree that the abstract provides no details on metric harmonization, subgroup analysis, or controls for confounders. The review is a qualitative summary of published studies rather than a formal meta-analysis, and the heterogeneity across imaging modalities, dataset sizes, and validation protocols precludes rigorous harmonization. We will revise the abstract to state that architecture comparisons are based on the metrics as originally reported, without adjustment for confounders, and to note this as a limitation of cross-study rankings. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: literature review with external citations only

full rationale

This paper is a literature review that summarizes and compares results from external studies on CNN architectures for ten cancers. It contains no original derivations, equations, fitted parameters, predictions, or self-referential claims that could reduce to its own inputs by construction. The abstract and available text reference distinct prior works for each cancer type without invoking self-citations as load-bearing premises or renaming results via ansatz. All comparisons draw from independent external datasets and protocols, satisfying the criterion for a self-contained review against external benchmarks.

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

Abstract-only review provides no information on free parameters, axioms, or invented entities used in any analysis.

how reviews work

0 comments

Cite this review

Pith. "Pith review of The Potential of Convolutional Neural Networks for Cancer Detection." pith.science (2026). https://pith.science/paper/RLHGPONY

@misc{pith2026241217155,
  author       = {Pith},
  title        = {Pith review of: The Potential of Convolutional Neural Networks for Cancer Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RLHGPONY}},
  note         = {Machine review of arXiv:2412.17155}
}

read the original abstract

Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different cancers have been identified in most of these advances that effectively use CNNs (Convolutional Neural Networks) for classification. The distinct architectures of CNNs used in each study concentrate on pattern recognition for different types of cancer across various datasets. The advantages and disadvantages of each approach are identified by comparing these architectures. This study explores the potential of integrating CNNs into clinical practice to complement traditional diagnostic methods. It also identifies the top-performing CNN architectures, highlighting their role in enhancing diagnostic capabilities in healthcare.

Figures

Figures reproduced from arXiv: 2412.17155 by the authors.

**Figure 6.** Fig6 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗

**Figure 7.** Fig7 [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

Figures from the paper (10 more)

**Figure 8.** Figure 8: Fig8 [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

**Figure 9.** Figure 9: Fig9 [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]

**Figure 10.** Figure 10: Fig10 [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]

**Figure 11.** Figure 11: Fig11 [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]

**Figure 12.** Figure 12: Fig12 [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]

**Figure 13.** Figure 13: Fig13 [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]

**Figure 14.** Figure 14: Architectural diagram of a CNN used for classifying colorectal cancer, highlighting key layers and connections49 Liver Cancer: In this paper, we propose a novel model ESP-UNet which avoids both undersegmentation and over-segmentation while segmenting the liver in CT …

**Figure 15.** Figure 15: Fig15 [PITH_FULL_IMAGE:figures/full_fig_p020_15.png]

**Figure 16.** Figure 16: Fig16 [PITH_FULL_IMAGE:figures/full_fig_p020_16.png]

**Figure 18.** Figure 18: Fig18 [PITH_FULL_IMAGE:figures/full_fig_p023_18.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages

[1]

These changes (gradients) indicate how much the weights and biases should change in order to reduce the cost function

Calculate the gradients: First, the changes in the cost function with respect to the weights and biases (called parameters) are computed. These changes (gradients) indicate how much the weights and biases should change in order to reduce the cost function

work page
[2]

Update the weights and biases: Next, the weights and biases are updated using the gradients

work page
[3]

Repeat the process: This process is repeated until the cost function is minimized. Weight and bias update formula in gradient descent: 𝑤 = 𝑤 − 𝛼 ⋅ 𝜕𝐽(𝑤, 𝑏) 𝜕𝑤 𝑏 = 𝑏 − 𝛼 ⋅ 𝜕𝐽(𝑤, 𝑏) 𝜕𝑏 9 (𝛼) is the learning rate (a number that determines how large the weight updates should be). Kernels in CNNs : Kernels (or filters) are basically the workings of a convoluti...

work page 2020
[4]

Then, the proposed model achieved 95.98% on the test data, which outperformed two pre-trained models, including GoogLeNet and MobileNet

Finally, a deep convolution neural network was designed in order to train on this dataset concerning mentioned images.39 This proposed model is optimized using an Adam optimizer with a learning rate of 0.001 along with the Cross-Entropy loss function. Then, the proposed model achieved 95.98% on the test data, which outperformed two pre-trained models, inc...

work page
[5]

Enhancing CT liver images using a modified dual-stage Gaussian filter (MDSGF)

work page
[6]

Segmenting the liver region with UNet

work page
[7]

Abdominal CT images often have low contrast and blurriness, making liver segmentation challenging

Detecting liver cancer using DCNN. Abdominal CT images often have low contrast and blurriness, making liver segmentation challenging. The proposed MDSGF method improves image quality by combining a dual-stage Gaussian filter with CLAHE and mean filtering, enabling more accurate liver segmentation.52 The input image is processed through two parallel UNet n...

work page
[8]

The background was discarded by eliminating zero-intensity pixels, and the image was refined using Otsu’s grayscale thresholding method

Preprocessing: DDSM images were converted to binary images, breast tissue and muscle intensity were enhanced, and unnecessary information was removed. The background was discarded by eliminating zero-intensity pixels, and the image was refined using Otsu’s grayscale thresholding method. Noise was reduced using the Wiener filter and signal-to-noise ratio (...

work page

Show all 21 references

[9]

These combined techniques improved the quality and clarity of breast cancer images.59 The DDSM images were randomly divided into 70% for training and 30% for testing

Wavelet Packet Decomposition (WPD): Used to eliminate non-stationary noise while preserving edges and texture by decomposing the image into sub-images across different directions. These combined techniques improved the quality and clarity of breast cancer images.59 The DDSM im...
[10]

A VGG-supported feedforward network
[11]

A 3D Convolutional Neural Network (3D CNN)
[12]

These methods can automatically identify significant features without requiring manual feature extraction

A Convolutional Long Short-Term Memory (ConvLSTM) network. These methods can automatically identify significant features without requiring manual feature extraction. The comparison of the models shows the following: • The VGG model has the highest number of learned parameters ...
[13]

VGG model: Peak AUC = 0.86, Mean AUC = 0.59
[14]

ConvLSTM model: Peak AUC = 0.98, Mean AUC = 0.81
[15]

The ConvLSTM model demonstrated the best performance with a Peak AUC of 0.98, outperforming the 3D CNN model (Peak AUC = 0.92) and the VGG model (Peak AUC = 0.86)

3D CNN model: Peak AUC = 0.92, Mean AUC = 0.69. The ConvLSTM model demonstrated the best performance with a Peak AUC of 0.98, outperforming the 3D CNN model (Peak AUC = 0.92) and the VGG model (Peak AUC = 0.86). Fig18. Architecture diagram of the model used for ovarian cancer ...
[16]

This stage evaluates the impact of the type of medical images (CT and ultrasound) on diagnostic results, with model tuning applied to different datasets

Stage 1: Medical images are fed into the Xception model for binary classification. This stage evaluates the impact of the type of medical images (CT and ultrasound) on diagnostic results, with model tuning applied to different datasets
[17]

Three optional architectures were proposed: • SIDC (Single Input Dual Channel): Combines input channels into a unified model

Stage 2: Multi-channel architectures based on Xception are applied for binary and multi-class classification tasks tailored to the needs of clinicians. Three optional architectures were proposed: • SIDC (Single Input Dual Channel): Combines input channels into a unified model....
[18]

Ultrasound DDTI: Accuracy = 0.980
[19]

Hospital_X Ultrasound: Accuracy = 0.987
[20]

CT scans (Left Side): Accuracy = 0.966
[21]

normal" patients (accuracy = 1.00) while the DIDC performs better with respect to

CT scans (Right Side): Accuracy = 0.970. Xception also stands out with extremely high values for predictive accuracy, NPV, recall, and F1 score. Although the fastest to run was ResNet10, the running times for Xception and DenseNet121 were pretty close. In this work, multi-chan...

2019 doi

Pith tools

Reviewed May 23, 2026 · model on record in the stance chip above.

[1] [1]

These changes (gradients) indicate how much the weights and biases should change in order to reduce the cost function

Calculate the gradients: First, the changes in the cost function with respect to the weights and biases (called parameters) are computed. These changes (gradients) indicate how much the weights and biases should change in order to reduce the cost function

work page

[2] [2]

Update the weights and biases: Next, the weights and biases are updated using the gradients

work page

[3] [3]

Repeat the process: This process is repeated until the cost function is minimized. Weight and bias update formula in gradient descent: 𝑤 = 𝑤 − 𝛼 ⋅ 𝜕𝐽(𝑤, 𝑏) 𝜕𝑤 𝑏 = 𝑏 − 𝛼 ⋅ 𝜕𝐽(𝑤, 𝑏) 𝜕𝑏 9 (𝛼) is the learning rate (a number that determines how large the weight updates should be). Kernels in CNNs : Kernels (or filters) are basically the workings of a convoluti...

work page 2020

[4] [4]

Then, the proposed model achieved 95.98% on the test data, which outperformed two pre-trained models, including GoogLeNet and MobileNet

Finally, a deep convolution neural network was designed in order to train on this dataset concerning mentioned images.39 This proposed model is optimized using an Adam optimizer with a learning rate of 0.001 along with the Cross-Entropy loss function. Then, the proposed model achieved 95.98% on the test data, which outperformed two pre-trained models, inc...

work page

[5] [5]

Enhancing CT liver images using a modified dual-stage Gaussian filter (MDSGF)

work page

[6] [6]

Segmenting the liver region with UNet

work page

[7] [7]

Abdominal CT images often have low contrast and blurriness, making liver segmentation challenging

Detecting liver cancer using DCNN. Abdominal CT images often have low contrast and blurriness, making liver segmentation challenging. The proposed MDSGF method improves image quality by combining a dual-stage Gaussian filter with CLAHE and mean filtering, enabling more accurate liver segmentation.52 The input image is processed through two parallel UNet n...

work page

[8] [8]

The background was discarded by eliminating zero-intensity pixels, and the image was refined using Otsu’s grayscale thresholding method

Preprocessing: DDSM images were converted to binary images, breast tissue and muscle intensity were enhanced, and unnecessary information was removed. The background was discarded by eliminating zero-intensity pixels, and the image was refined using Otsu’s grayscale thresholding method. Noise was reduced using the Wiener filter and signal-to-noise ratio (...

work page