MpoxSLDNet: A Novel CNN Model for Detecting Monkeypox Lesions and Performance Comparison with Pre-trained Models

• URL: http://arxiv.org/abs/2405.21016v1
• Date: Fri, 31 May 2024 17:05:59 GMT
• Title: MpoxSLDNet: A Novel CNN Model for Detecting Monkeypox Lesions and Performance Comparison with Pre-trained Models
• Authors: Fatema Jannat Dihan, Saydul Akbar Murad, Abu Jafar Md Muzahid, K. M. Aslam Uddin, Mohammed J. F. Alenazi, Anupam Kumar Bairagi, Sujit Biswas,
• Abstract summary: 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.
• Score: 0.7628351525795085
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Monkeypox virus (MPXV) is a zoonotic virus that poses a significant threat to public health, particularly in remote parts of Central and West Africa. Early detection of monkeypox lesions is crucial for effective treatment. However, due to its similarity with other skin diseases, monkeypox lesion detection is a challenging task. To detect monkeypox, many researchers used various deep-learning models such as MobileNetv2, VGG16, ResNet50, InceptionV3, DenseNet121, EfficientNetB3, MobileNetV2, and Xception. However, these models often require high storage space due to their large size. This study aims to improve the existing challenges by introducing a CNN model named MpoxSLDNet (Monkeypox Skin Lesion Detector Network) to facilitate early detection and categorization of Monkeypox lesions and Non-Monkeypox lesions in digital images. Our model represents a significant advancement in the field of monkeypox lesion detection by offering superior performance metrics, including precision, recall, F1-score, accuracy, and AUC, compared to traditional pre-trained models such as VGG16, ResNet50, and DenseNet121. The key novelty of our approach lies in MpoxSLDNet's ability to achieve high detection accuracy while requiring significantly less storage space than existing models. By addressing the challenge of high storage requirements, MpoxSLDNet presents a practical solution for early detection and categorization of monkeypox lesions in resource-constrained healthcare settings. In this study, we have used "Monkeypox Skin Lesion Dataset" comprising 1428 skin images of monkeypox lesions and 1764 skin images of Non-Monkeypox lesions. Dataset's limitations could potentially impact the model's ability to generalize to unseen cases. However, the MpoxSLDNet model achieved a validation accuracy of 94.56%, compared to 86.25%, 84.38%, and 67.19% for VGG16, DenseNet121, and ResNet50, respectively.

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)
• Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining [85.08169822181685]
  This paper introduces a novel Mamba-based model, Swin-UMamba, designed specifically for medical image segmentation tasks. Swin-UMamba demonstrates superior performance with a large margin compared to CNNs, ViTs, and latest Mamba-based models.
  arXiv  Detail & Related papers  (2024-02-05T18:58:11Z)
• 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)
• Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study [1.9395755884693817]
  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.
  arXiv  Detail & Related papers  (2022-07-06T09:09:28Z)
• 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)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• 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)
• 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)
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection [86.81773672627406]
  Clusters of viral pneumonia during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images.
  arXiv  Detail & Related papers  (2020-03-27T11:32:18Z)
• Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks [0.0]
  coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Five pre-trained convolutional neural network based models have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs.
  arXiv  Detail & Related papers  (2020-03-24T13:50:23Z)

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.