Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 13, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

CBAM-Xception: An Attention-Guided Framework for Skin Cancer Classification.

Faysal Ahmmed1, Ajmy Alaly2, Samanta Mehnaj2

  • 1Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, 1229, Bangladesh. 22-47069-1@student.aiub.edu.

Journal of Imaging Informatics in Medicine
|May 12, 2026
PubMed
Summary

Related Concept Videos

Skin Cancer01:30

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Explainable AI in kidney stone detection and segmentation: a mini review.

Frontiers in digital health·2026
Same author

Multi-modal federated learning with differential privacy for privacy-preserving healthcare AI.

Scientific reports·2026
Same author

Explainable deep learning for early diagnosis of chronic kidney disease from CT images in Bangladeshi patients.

Scientific reports·2026
Same author

Early detection of chronic kidney disease using deep learning: a Mini review.

Frontiers in digital health·2026
Same author

Enhanced cervical cancer diagnosis using a novel Bayesian fusion ensemble method with explainable AI.

Scientific reports·2026
Same author

Molecular Scale Hydrophobicity and Adsorption Thermodynamics on Hydrophobic-Charged Surfaces.

ACS nano·2026
Same journal

How is Bias Learned in Medical Image Analysis Models? An Exploration of the Encoding of Demographic Information in Deep Learning Models Trained to Detect Abnormalities on Chest X-Rays.

Journal of imaging informatics in medicine·2026
Same journal

AI-Based Opportunistic CT Risk Assessment Using TotalSegmentator in Osteoporotic Vertebral Fractures.

Journal of imaging informatics in medicine·2026
Same journal

A Computationally Efficient and Improved Brain Tumor Recognition System by MRI-Segmentation Integrated Classification Network.

Journal of imaging informatics in medicine·2026
Same journal

Ultrasound Domain Adaptation for Robust Kidney Segmentation via Spectral-Similarity-Guided Translation.

Journal of imaging informatics in medicine·2026
Same journal

Dynamic Fuzzy-Gaussian Modeling (DynFGM): A Kurtosis-Adaptive Unsupervised Framework for Automated Adipose Tissue Segmentation in Abdominal MRI.

Journal of imaging informatics in medicine·2026
Same journal

Computerized Classification Method for Glioma Molecular Subtypes on Brain MR Images Using SAM-Med3D with Low-Rank Adaptation.

Journal of imaging informatics in medicine·2026
See all related articles
This summary is machine-generated.

This study introduces CBAM-Xception, an explainable deep learning model for accurate skin cancer diagnosis. It improves classification by focusing on relevant lesion features, outperforming other models on key datasets.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Dermatology

Background:

  • Skin cancer diagnosis requires early and accurate detection for improved patient outcomes.
  • Current deep learning models for skin lesion classification face challenges like limited interpretability and class imbalance.
  • Automated diagnosis systems need to focus on clinically relevant features while minimizing noise.

Purpose of the Study:

  • To introduce CBAM-Xception, an explainable attention-guided deep learning model for enhanced skin lesion classification.
  • To improve the accuracy and interpretability of automated skin cancer diagnosis.
  • To address limitations of existing models, including class imbalance and irrelevant feature extraction.

Main Methods:

  • Developed an attention-guided deep learning model (CBAM-Xception) integrating Xception backbone and Convolutional Block Attention Module (CBAM).
Keywords:
CBAMCLAHEDeep learningExplainable AIGrad-CAM++HAM10000ISIC-2019Skin cancer classificationXception

Related Experiment Videos

Last Updated: May 13, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

  • Applied Contrast Limited Adaptive Histogram Equalization (CLAHE) for image contrast enhancement.
  • Utilized geometric and color augmentation to mitigate class imbalance on HAM10000 and ISIC 2019 datasets.
  • Froze initial Xception layers for fine-tuning and employed Grad-CAM++ for visualization.

    • Main Results:

    • CBAM-Xception achieved high accuracy: 98.62% (AUC: 0.9997) on HAM10000 and 93.66% (AUC: 0.9939) on ISIC 2019.
    • The model demonstrated superior performance compared to MobileNet and EfficientNet baselines.
    • Grad-CAM++ visualizations confirmed the model's focus on clinically relevant lesion areas, enhancing interpretability.

    Conclusions:

    • CBAM-Xception offers a reliable solution for automated skin cancer diagnosis by combining high accuracy, interpretability, and robustness.
    • The model effectively addresses class imbalance and focuses on discriminative lesion features.
    • Further research may be needed to assess generalizability across diverse clinical settings and computational resources.