Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study

• URL: http://arxiv.org/abs/2207.03342v1
• Date: Wed, 6 Jul 2022 09:09:28 GMT
• Title: Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study
• Authors: Shams Nafisa Ali, Md. Tazuddin Ahmed, Joydip Paul, Tasnim Jahan, S. M. Sakeef Sani, Nawsabah Noor, Taufiq Hasan
• Abstract summary: Recent monkeypox outbreak has become a public health concern due to its rapid spread in more than 40 countries outside Africa. Computer-assisted detection of monkeypox lesions could be beneficial for surveillance and rapid identification of suspected cases. Deep learning methods have been found effective in the automated detection of skin lesions.
• Score: 1.9395755884693817
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The recent monkeypox outbreak has become a public health concern due to its rapid spread in more than 40 countries outside Africa. Clinical diagnosis of monkeypox in an early stage is challenging due to its similarity with chickenpox and measles. In cases where the confirmatory Polymerase Chain Reaction (PCR) tests are not readily available, computer-assisted detection of monkeypox lesions could be beneficial for surveillance and rapid identification of suspected cases. Deep learning methods have been found effective in the automated detection of skin lesions, provided that sufficient training examples are available. However, as of now, such datasets are not available for the monkeypox disease. In the current study, we first develop the ``Monkeypox Skin Lesion Dataset (MSLD)" consisting skin lesion images of monkeypox, chickenpox, and measles. The images are mainly collected from websites, news portals, and publicly accessible case reports. Data augmentation is used to increase the sample size, and a 3-fold cross-validation experiment is set up. In the next step, several pre-trained deep learning models, namely, VGG-16, ResNet50, and InceptionV3 are employed to classify monkeypox and other diseases. An ensemble of the three models is also developed. ResNet50 achieves the best overall accuracy of $82.96(\pm4.57\%)$, while VGG16 and the ensemble system achieved accuracies of $81.48(\pm6.87\%)$ and $79.26(\pm1.05\%)$, respectively. A prototype web-application is also developed as an online monkeypox screening tool. While the initial results on this limited dataset are promising, a larger demographically diverse dataset is required to further enhance the generalizability of these models.

Related papers

• 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. 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)
• Attention Based Feature Fusion Network for Monkeypox Skin Lesion Detection [0.09642500063568188]
  Recent monkeypox outbreak has raised significant public health concerns. Deep learning algorithms can be used to identify diseases, including COVID-19. We introduce a lightweight model that merges two pre-trained architectures to classify human monkeypox disease.
  arXiv  Detail & Related papers  (2024-08-13T05:21:03Z)
• MpoxSLDNet: A Novel CNN Model for Detecting Monkeypox Lesions and Performance Comparison with Pre-trained Models [0.7628351525795085]
  This study introduces MpoxSLDNet (Monkeypox Skin Lesion Detector Network) to facilitate early detection and categorization of monkeypox lesions in digital images. MpoxSLDNet offers superior performance metrics, including precision, recall, F1-score, accuracy, and AUC, compared to traditional pre-trained models such as VGG16, ResNet50, and DenseNet121. The model achieved a validation accuracy of 94.56%, compared to 86.25%, 84.38%, and 67.19% for VGG16, DenseNet121, and ResNet50, respectively.
  arXiv  Detail & Related papers  (2024-05-31T17:05:59Z)
• Monkeypox disease recognition model based on improved SE-InceptionV3 [0.0]
  This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into the InceptionV3 framework to enhance monkeypox disease detection. Our model demonstrates a noteworthy accuracy of 96.71% on the test set, outperforming conventional methods and deep learning models.
  arXiv  Detail & Related papers  (2024-03-15T08:01:44Z)
• How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers? [49.35105290167996]
  Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
  arXiv  Detail & Related papers  (2023-08-17T20:40:30Z)
• 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)
• Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16 [0.0]
  We introduce a newly developed "Monkeypox2022" dataset that is publicly available to use and can be obtained from our shared GitHub repository. We propose and evaluate a modified VGG16 model, which includes two distinct studies: Study One and Two. Our exploratory computational results indicate that our suggested model can identify Monkeypox patients with an accuracy of $97pm1.8%$.
  arXiv  Detail & Related papers  (2022-06-04T00:34:15Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Self-Supervised U-Net for Segmenting Flat and Sessile Polyps [63.62764375279861]
  Development of colorectal polyps is one of the earliest signs of cancer. Early detection and resection of polyps can greatly increase survival rate to 90%. Computer-Aided Diagnosis systems(CADx) has been proposed that detect polyps by processing the colonoscopic videos.
  arXiv  Detail & Related papers  (2021-10-17T09:31:20Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.