# A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity arXiv: https://arxiv.org/abs/2306.14169 · HTML: https://mlsanity.com/paper/2306.14169/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 15 of its 53 references that are in the map. - First posted on arXiv 25 Jun 2023. ## Builds on (most cited first, 15 of 15) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018) - [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016) - [Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study](https://mlsanity.com/paper/2207.03342/) (2022) - [Monkeypox virus detection using pre-trained deep learning-based approaches](https://mlsanity.com/paper/2209.04444/) (2022) - [A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram](https://mlsanity.com/paper/2009.04402/) (2020) - [Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16](https://mlsanity.com/paper/2206.01862/) (2022) - [Monkeypox Image Data collection](https://mlsanity.com/paper/2206.01774/) (2022) - [Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms](https://mlsanity.com/paper/2211.15459/) (2022) - [Color Invariant Skin Segmentation](https://mlsanity.com/paper/2204.09882/) (2022) - [AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments](https://mlsanity.com/paper/2211.14313/) (2022) ## Built on by (most cited first, 17 of 17) - [MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus Infection](https://mlsanity.com/paper/2411.10888/) (2024) - [Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere](https://mlsanity.com/paper/2308.13492/) (2023) - [Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis](https://mlsanity.com/paper/2508.04573/) (2025) - [Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning](https://mlsanity.com/paper/2509.00745/) (2025) - [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026) - [An Attention-Guided Deep Learning Approach for Classifying 39 Skin Lesion Types](https://mlsanity.com/paper/2501.05991/) (2025) - [Adversarial Masked Image Inpainting for Robust Detection of Mpox and Non-Mpox](https://mlsanity.com/paper/2310.06318/) (2023) - [Monkeypox disease recognition model based on improved SE-InceptionV3](https://mlsanity.com/paper/2403.10087/) (2024) - [Adapting an Artificial Intelligence Sexually Transmitted Diseases Symptom Checker Tool for Mpox Detection: The HeHealth Experience](https://mlsanity.com/paper/2404.16885/) (2024) - [MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection](https://mlsanity.com/paper/2409.04218/) (2024) - [A Cascaded Dilated Convolution Approach for Mpox Lesion Classification](https://mlsanity.com/paper/2412.10106/) (2024) - [Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning and Vision Transformers: A Comprehensive Analysis](https://mlsanity.com/paper/2505.01429/) (2025) - [Smart Waste Management System for Makkah City using Artificial Intelligence and Internet of Things](https://mlsanity.com/paper/2505.19040/) (2025) - [Computer Vision for Real-Time Monkeypox Diagnosis on Embedded Systems](https://mlsanity.com/paper/2507.17123/) (2025) - [Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting](https://mlsanity.com/paper/2512.08733/) (2025) - [Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors](https://mlsanity.com/paper/2605.05034/) (2026) - [An Attention-Guided Global and Local Fusion Framework for Lesion-Focused Image Classification](https://mlsanity.com/paper/2609.04791/) (2026) ## Abstract (arXiv) The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. 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. To address this challenge, we present the "Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)" as a follow-up to the previously released openly accessible dataset, one of the first datasets containing mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. In order to reduce the impact of racial bias, we utilize a color space data augmentation method to increase skin color variability during training. Additionally, by leveraging transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images, we achieved the best overall accuracy of 83.59±2.11%. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient.