CoChat
Sign Up
Cited by 5Cutaneous Melanoma Detection and ManagementAI in cancer detectionArtificial Intelligence in Healthcare

Machine learning approaches for skin neoplasm diagnosis

Open access
Asaduzzaman, Abu, Thompson, Christian C., Uddin, Md. Jashim·2024·ACS Omega
DOI 10.1021/acsomega.4c03640PMC PMC11292652
Open this paper in CoChat — free account

Abstract

Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) discriminator is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original data set for preventing model overfitting. Synthetic minority oversampling technique (SMOTE) is employed to rectify class imbalances in the original data set. To accurately classify skin images, the following machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN-SVM. Experimental results using the HAM10000 data set demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN-SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN (discriminator) and SMOTE, the LDA, SVM, CNN, and ensemble CNN-SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and data sets in our next endeavor.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acsomega.4c03640

Cites

60 of 60 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions.

    “However, most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions (14–22).”

    From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [19]

    The Introduction identifies “interpretability issues with complex models,” and the Conclusions emphasize improvements in classification accuracy. But neither passage says that most prior studies pursued increasingly complex architectures or gave insufficient attention to explaining model decisions. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Other approaches employ GAN-based generation, offering realistic samples at the cost of increased computational complexity and training instability.

    “Other approaches employ GAN-based generation (19), offering realistic samples at the cost of increased computational complexity and training instability.”

    From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [19]

    The Related Work section (‘GAN: Generator and Discriminator’; page unavailable) says GANs generate images that appear real and that training them can be computationally costly. It also mentions rigorous hyperparameter tuning, but does not state that GAN training is unstable. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

1 retracted

Flagged first, then by citations

2 of the 62 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Related

Authors
Asaduzzaman, A., Thompson, C. C., & Uddin, M. J.
Affiliations
  • Wichita State University (US)
  • Vanderbilt University (US)
Corresponding author
Uddin, Md. Jashim
ORCID
Venue
ACS Omega
Publisher
American Chemical Society (ACS)
Published
2024-07-15
Volume
9
Issue
30
Pages
32853-32863
ISSN
2470-1343 (ISSN-L)
DOI
10.1021/acsomega.4c03640
PMC
PMC11292652
OpenAlex
W4400643514
Cited by
3
References
62
Citations by year
2026: 2 · 2025: 1
FWCI
0.51
Type
journal article
Language
en
Open access
Licence
Locations
  • ACS Omega (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Holmes Museum Of Anthropology (Wichita State University) (submittedVersion, cc-by)
Funders
  • National Institutes of Health (1R01CA260958-01A1)
  • Phi Beta Psi Sorority Trust (AWD00000652, AWD00001248)
Topics
  • Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
computer-aided diagnosis, melanoma, melanocytic nevi, skin lesion classification, generative adversarial networks, fake image detection, SMOTE, class imbalance, HAM10000 dataset, convolutional neural networks, linear discriminant analysis, support vector machine

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review