2021/06/30 by Sk Imran Hossain, Jocelyn de Goër de Hervé, Jocelyn de Goër de Herve +30 · 43 citations
Computer Science · Engineering · Medicine · #AI in cancer detection #Artificial intelligence #Computer science #Convolutional neural network #Cutaneous Melanoma Detection and Management #Deep learning #Dermatology #Digital Imaging for Blood Diseases #Lyme disease #Machine learning #Medicine #Pattern recognition (psychology) #Skin lesion #Transfer of learning #cs.CV #cs.LG #eess.IV
paper · pdf · doi:10.1016/j.cmpb.2022.106624
published in Computer Methods and Programs in Biomedicine 215, 106624 (Elsevier BV)
openalex publication_date 2022/01/10 · arxiv created 2022/02/15 · arxiv updated 2022/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Lyme disease which is one of the most common infectious vector-borne diseases manifests itself in most cases with erythema migrans (EM) skin lesions. Recent studies show that convolutional neural networks (CNNs) perform well to identify skin lesions from images. Lightweight CNN based pre-scanner applications for resource-constrained mobile devices can help users with early diagnosis of Lyme disease and prevent the transition to a severe late form thanks to appropriate antibiotic therapy. Also, resource-intensive CNN based robust computer applications can assist non-expert practitioners with an accurate diagnosis. The main objective of this study is to extensively analyze the effectiveness of CNNs for diagnosing Lyme disease from images and to find out the best CNN architectures considering resource constraints. First, we created an EM dataset with the help of expert dermatologists from Clermont-Ferrand University Hospital Center of France. Second, we benchmarked this dataset for twenty-three CNN architectures customized from VGG, ResNet, DenseNet, MobileNet, Xception, NASNet, and EfficientNet architectures in terms of predictive performance, computational complexity, and statistical significance. Third, to improve the performance of the CNNs, we used custom transfer learning from ImageNet pre-trained models as well as pre-trained the CNNs with the skin lesion dataset HAM10000. Fourth, for model explainability, we utilized Gradient-weighted Class Activation Mapping to visualize the regions of input that are significant to the CNNs for making predictions. Fifth, we provided guidelines for model selection based on predictive performance and computational complexity.