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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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.