Dermatology 2.0: Deploying YOLOv11 for Accurate and Accessible Skin Disease Detection: A Web-Based Approach

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2025-02-14 DOI:10.1002/ima.70050

Adnan Hameed, Said Khalid Shah, Sajid Ullah Khan, Sultan Alanazi, Shabbab Ali Algamdi

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引用次数: 0

Abstract

Skin disorders are common and require diagnosis and treatment in a timely manner. In traditional diagnostics, great demands are made on the time and interpretation of the results. To cope with this, we introduce YOLOv11, an enhanced deep learning model designed for skin disease detection and classification. The model integrates EfficientNetB0 as the backbone for feature extraction and ResNet50 in the head for robust classification and localization. Our model was trained on a dataset of 10 common skin diseases to ensure robustness and accuracy; we were able to classify the diseases with a mean Average Precision (mAP) of 89.8%, a precision of 90%, and a recall of 88% on the test dataset. This model was developed in the form of a web application based on Streamlit, which was used for easy uploading of pictures by both clinicians and patients for threshold diagnostics. This upsurge in technology allows for treatment without visitation, making skin disease diagnosis more dynamic.

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皮肤病学2.0：部署YOLOv11用于准确和可访问的皮肤病检测：基于web的方法

皮肤病很常见，需要及时诊断和治疗。在传统的诊断中，对时间和结果的解释提出了很高的要求。为了解决这个问题，我们引入了YOLOv11，一种增强的深度学习模型，用于皮肤病的检测和分类。该模型集成了高效netb0作为特征提取的主干和ResNet50作为头部的鲁棒分类和定位。我们的模型是在10种常见皮肤病的数据集上训练的，以确保稳健性和准确性；在测试数据集上，我们能够以89.8%的平均精度（mAP）， 90%的精度和88%的召回率对疾病进行分类。该模型以基于Streamlit的web应用程序的形式开发，用于临床医生和患者方便地上传图片进行阈值诊断。这一技术的发展使得无需探视的治疗成为可能，使皮肤病的诊断更加动态。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.