Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis

• URL: http://arxiv.org/abs/2508.04573v1
• Date: Wed, 06 Aug 2025 15:57:49 GMT
• Title: Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis
• Authors: Enam Ahmed Taufik, Abdullah Khondoker, Antara Firoz Parsa, Seraj Al Mahmud Mostafa,
• Abstract summary: This study proposes a deep learning framework for multi-class skin disease classification.<n>We benchmark the performance of pre-trained convolutional neural networks (DenseNet201, Efficient-NetB5) and transformer-based models (ViT, Swin Transformer, DinoV2 Large)<n>Results show that DinoV2 with RGB pre-processing achieves the highest accuracy (up to 93%) and F1-scores across all variants.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate skin disease classification is a critical yet challenging task due to high inter-class similarity, intra-class variability, and complex lesion textures. While deep learning-based computer-aided diagnosis (CAD) systems have shown promise in automating dermatological assessments, their performance is highly dependent on image pre-processing and model architecture. This study proposes a deep learning framework for multi-class skin disease classification, systematically evaluating three image pre-processing techniques: standard RGB, CMY color space transformation, and Contrast Limited Adaptive Histogram Equalization (CLAHE). We benchmark the performance of pre-trained convolutional neural networks (DenseNet201, Efficient-NetB5) and transformer-based models (ViT, Swin Transformer, DinoV2 Large) using accuracy and F1-score as evaluation metrics. Results show that DinoV2 with RGB pre-processing achieves the highest accuracy (up to 93%) and F1-scores across all variants. Grad-CAM visualizations applied to RGB inputs further reveal precise lesion localization, enhancing interpretability. These findings underscore the importance of effective pre-processing and model choice in building robust and explainable CAD systems for dermatology.

Related papers

• Swin Transformer for Robust CGI Images Detection: Intra- and Inter-Dataset Analysis across Multiple Color Spaces [1.024113475677323]
  This study aims to address the challenge of distinguishing computer-generated imagery (CGI) from authentic digital images.<n>It proposes a Swin Transformer based model for accurate differentiation between natural and synthetic images.<n>The model's performance was tested across all color schemes, with the RGB color scheme yielding the highest accuracy for each dataset.
  arXiv  Detail & Related papers  (2025-05-22T05:43:40Z)
• Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches [1.1011387049911827]
  Photoplethysmography is a widely used non-invasive physiological sensing technique, suitable for various clinical applications.<n>Machine learning methods are increasingly supported by machine learning methods, raising the question of the most appropriate input representation and model choice.<n>We address this gap in the research landscape by a comprehensive benchmarking study covering three kinds of input representations, interpretable features, image representations and raw waveforms.
  arXiv  Detail & Related papers  (2025-02-27T10:17:16Z)
• GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis [44.99833362998488]
  We present a novel approach that combines 2D Gaussian splatting with the Transformer UNet architecture for automated skin cancer diagnosis.<n>Our findings illustrate significant advancements in the precision of segmentation and classification.<n>This integration sets new benchmarks in the field and highlights the potential for further research into multi-task medical image analysis methodologies.
  arXiv  Detail & Related papers  (2025-02-23T23:28:47Z)
• Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI [2.3149142745203326]
  Skin diseases affect over a third of the global population, yet their impact is often underestimated. Deep learning techniques have shown much promise for various tasks, including dermatological disease identification. This study uses a skin disease dataset with 31 classes and compares it with all versions of Vision Transformers, Swin Transformers and DivoV2.
  arXiv  Detail & Related papers  (2024-07-20T05:38:00Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• LesionAid: Vision Transformers-based Skin Lesion Generation and Classification [0.0]
  This research proposes a novel multi-class prediction framework that classifies skin lesions based on ViT and ViTGAN. The framework consists of four main phases: ViTGANs, Image processing, and explainable AI.
  arXiv  Detail & Related papers  (2023-02-02T13:52:54Z)
• Machine Learning Method for Functional Assessment of Retinal Models [5.396946042201311]
  We introduce the functional assessment (FA) of retinal models, which describes the concept of evaluating their performance. We present a machine learning method for FA: we feed traditional machine learning classifiers with RGC responses generated by retinal models. We show that differences in the structure of datasets result in largely divergent performance of the retinal model.
  arXiv  Detail & Related papers  (2022-02-05T00:35:38Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology [42.531674974834544]
  We propose a CNN-based model that maintains fast computations similar to the knowledge-driven methods without excessive hardware requirements. We create a training dataset using FocusPath which encompasses diverse tissue slides across nine different stain colors. In our attempt to reduce the CNN complexity, we find with surprise that even trimming down the CNN to the minimal level, it still achieves a highly competitive performance.
  arXiv  Detail & Related papers  (2020-07-11T20:52:01Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.