Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning and Vision Transformers: A Comprehensive Analysis

• URL: http://arxiv.org/abs/2505.01429v1
• Date: Thu, 03 Apr 2025 19:45:22 GMT
• Title: Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning and Vision Transformers: A Comprehensive Analysis
• Authors: Md. Zahid Hossain, Md. Rakibul Islam, Most. Sharmin Sultana Samu,
• Abstract summary: mpox is a zoonotic viral illness that poses a significant public health concern.<n>It is difficult to make an early clinical diagnosis because of how closely its symptoms match those of measles and chickenpox.<n>Medical imaging combined with deep learning (DL) techniques has shown promise in improving disease detection by analyzing affected skin areas.<n>Our study explore the feasibility to train deep learning and vision transformer-based models from scratch with publicly available skin lesion image dataset.
• Score: 0.20482269513546453
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Since mpox can spread from person to person, it is a zoonotic viral illness that poses a significant public health concern. It is difficult to make an early clinical diagnosis because of how closely its symptoms match those of measles and chickenpox. Medical imaging combined with deep learning (DL) techniques has shown promise in improving disease detection by analyzing affected skin areas. Our study explore the feasibility to train deep learning and vision transformer-based models from scratch with publicly available skin lesion image dataset. Our experimental results show dataset limitation as a major drawback to build better classifier models trained from scratch. We used transfer learning with the help of pre-trained models to get a better classifier. The MobileNet-v2 outperformed other state of the art pre-trained models with 93.15% accuracy and 93.09% weighted average F1 score. ViT B16 and ResNet-50 also achieved satisfactory performance compared to already available studies with accuracy 92.12% and 86.21% respectively. To further validate the performance of the models, we applied explainable AI techniques.

Related papers

• An empirical study for the early detection of Mpox from skin lesion images using pretrained CNN models leveraging XAI technique [0.471858286267785]
  Mpox is a zoonotic disease caused by the Mpox virus, which shares similarities with other skin conditions.<n>This study aims to evaluate the effectiveness of pre-trained CNN models for the early detection of monkeypox.<n>It also seeks to enhance model interpretability using Grad-CAM an XAI technique.
  arXiv  Detail & Related papers  (2025-07-21T17:30:08Z)
• Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback [43.1078084014722]
  We propose a novel framework, coined MAGIC, that synthesizes clinically accurate skin disease images for data augmentation.<n>Our method creatively translates expert-defined criteria into actionable feedback for image synthesis of DMs.
  arXiv  Detail & Related papers  (2025-06-14T03:15:09Z)
• Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography [0.0]
  We evaluate radiomics-based and deep learning-based approaches for disease detection in chest radiography.<n>Deep learning models learn directly from image data, while radiomics-based models extract handcrafted features.<n>These findings provide statistically validated, data-driven recommendations for model selection in diagnostic AI.
  arXiv  Detail & Related papers  (2025-04-16T16:54:37Z)
• Artificial Intelligence-Driven Prognostic Classification of COVID-19 Using Chest X-rays: A Deep Learning Approach [0.0]
  This study presents a high-accuracy deep learning model for classifying COVID-19 severity (Mild, Moderate, and Severe) using Chest X-ray images.<n>Our model achieved an average accuracy of 97%, with specificity of 99%, sensitivity of 87%, and an F1-score of 93.11%.<n>These results demonstrate the model's potential for real-world clinical applications.
  arXiv  Detail & Related papers  (2025-03-17T15:27:21Z)
• Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity [43.108040967674185]
  Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD)<n>This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD.
  arXiv  Detail & Related papers  (2025-02-18T12:01:55Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas.<n>This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Phikon-v2, A large and public feature extractor for biomarker prediction [42.52549987351643]
  We train a vision transformer using DINOv2 and publicly release one iteration of this model for further experimentation, coined Phikon-v2. While trained on publicly available histology slides, Phikon-v2 surpasses our previously released model (Phikon) and performs on par with other histopathology foundation models (FM) trained on proprietary data.
  arXiv  Detail & Related papers  (2024-09-13T20:12:29Z)
• 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)
• Symptom-based Machine Learning Models for the Early Detection of COVID-19: A Narrative Review [0.0]
  Machine learning models can analyze large datasets, incorporating patient-reported symptoms, clinical data, and medical imaging. In this paper, we provide an overview of the landscape of symptoms-only machine learning models for predicting COVID-19, including their performance and limitations. The review will also examine the performance of symptom-based models when compared to image-based models.
  arXiv  Detail & Related papers  (2023-12-08T01:41:42Z)
• A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity [1.846958522363092]
  'Mpox', formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods are powerful in learning complex data representations, but their efficacy largely depends on adequate training data.
  arXiv  Detail & Related papers  (2023-06-25T08:23:44Z)
• Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis [48.64462717254158]
  We developed a self-supervised contrastive learning approach, EchoCLR, to catered to echocardiogram videos. When fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improved classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) EchoCLR is unique in its ability to learn representations of medical videos and demonstrates that SSL can enable label-efficient disease classification from small, labeled datasets.
  arXiv  Detail & Related papers  (2022-07-23T19:17:26Z)
• BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis [42.917164607812886]
  Vision-and-language(V&L) models take image and text as input and learn to capture the associations between them. BERTHop is a transformer-based model based on PixelHop++ and VisualBERT, for better capturing the associations between the two modalities.
  arXiv  Detail & Related papers  (2021-08-10T21:51:25Z)
• Predictive Analysis of Diabetic Retinopathy with Transfer Learning [0.0]
  This paper studies the performance of CNN architectures for Diabetic Retinopathy Classification with the help of Transfer Learning. The results indicate that Transfer Learning with ImageNet weights using VGG 16 model demonstrates the best classification performance with the best Accuracy of 95%.
  arXiv  Detail & Related papers  (2020-11-08T18:54:57Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)

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.