A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images

• URL: http://arxiv.org/abs/2305.18489v1
• Date: Mon, 29 May 2023 13:14:05 GMT
• Title: A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images
• Authors: Mattia Giovanni Campana, Marco Colussi, Franca Delmastro, Sergio Mascetti, Elena Pagani
• Abstract summary: Mpox infection is the appearance of skin rashes and eruptions, which can drive people to seek medical advice. A technology that might help perform a preliminary screening based on the aspect of skin lesions is the use of Machine Learning for image classification. In this work, we investigate the adoption of Deep Learning to detect mpox from skin lesion images.
• Score: 5.039813366558306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent months, the monkeypox (mpox) virus -- previously endemic in a limited area of the world -- has started spreading in multiple countries until being declared a ``public health emergency of international concern'' by the World Health Organization. The alert was renewed in February 2023 due to a persisting sustained incidence of the virus in several countries and worries about possible new outbreaks. Low-income countries with inadequate infrastructures for vaccine and testing administration are particularly at risk. A symptom of mpox infection is the appearance of skin rashes and eruptions, which can drive people to seek medical advice. A technology that might help perform a preliminary screening based on the aspect of skin lesions is the use of Machine Learning for image classification. However, to make this technology suitable on a large scale, it should be usable directly on mobile devices of people, with a possible notification to a remote medical expert. In this work, we investigate the adoption of Deep Learning to detect mpox from skin lesion images. The proposal leverages Transfer Learning to cope with the scarce availability of mpox image datasets. As a first step, a homogenous, unpolluted, dataset is produced by manual selection and preprocessing of available image data. It will also be released publicly to researchers in the field. Then, a thorough comparison is conducted amongst several Convolutional Neural Networks, based on a 10-fold stratified cross-validation. The best models are then optimized through quantization for use on mobile devices; measures of classification quality, memory footprint, and processing times validate the feasibility of our proposal. Additionally, the use of eXplainable AI is investigated as a suitable instrument to both technically and clinically validate classification outcomes.

Related papers

• A Recent Survey of the Advancements in Deep Learning Techniques for Monkeypox Disease Detection [0.7179624965454197]
  Monkeypox (MPox) is a zoonotic infectious disease induced by the MPox Virus. This survey paper provides an extensive analysis of deep learning (DL) methods for the automatic detection of MPox in skin lesion images.
  arXiv  Detail & Related papers  (2023-11-06T18:18:42Z)
• 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)
• 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)
• A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19 [64.88701052813462]
  Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios. In this paper, we primarily focus on the AI techniques of masked facial detection and related datasets.
  arXiv  Detail & Related papers  (2022-01-13T03:28:20Z)
• FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis [82.2511780233828]
  We propose a novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various medical image analysis tasks. Specifically, FIBA leverages a trigger function in the frequency domain that can inject the low-frequency information of a trigger image into the poisoned image by linearly combining the spectral amplitude of both images.
  arXiv  Detail & Related papers  (2021-12-02T11:52:17Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks [6.280530476948474]
  Coccidioidomycosis is the most common systemic mycosis in dogs in the southwestern United States. We apply machine learning models to provide accurate and interpretable predictions of Coccidioidomycosis.
  arXiv  Detail & Related papers  (2021-01-30T18:06:40Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Deep Neural Networks for COVID-19 Detection and Diagnosis using Images and Acoustic-based Techniques: A Recent Review [0.36550217261503676]
  The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus.
  arXiv  Detail & Related papers  (2020-12-10T19:52:12Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)

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.