SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images
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| Argitaratua izan da: | Machine Learning and Knowledge Extraction vol. 7, no. 4 (2025), p. 157-184 |
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| Beste egile batzuk: | , , , , , |
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MDPI AG
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| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3286316558 | ||
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| 022 | |a 2504-4990 | ||
| 024 | 7 | |a 10.3390/make7040157 |2 doi | |
| 035 | |a 3286316558 | ||
| 045 | 2 | |b d20251001 |b d20251231 | |
| 100 | 1 | |a Amir, Sohel |u Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh; amir.cse@diu.edu.bd (A.S.); turjy15-6289@s.diu.edu.bd (R.C.D.T.); bappy15-6155@s.diu.edu.bd (S.P.B.); assaduzzaman.cse@diu.edu.bd (M.A.) | |
| 245 | 1 | |a SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Lyme disease, caused by the Borrelia burgdorferi bacterium and transmitted through black-legged (deer) tick bites, is becoming increasingly prevalent globally. According to data from the Lyme Disease Association, the number of cases has surged by more than 357% over the past 15 years. According to the Infectious Disease Society of America, traditional diagnostic methods are often slow, potentially allowing bacterial proliferation and complicating early management. This study proposes a novel hybrid deep learning framework to classify Lyme disease rashes, addressing the global prevalence of the disease caused by the Borrelia burgdorferi bacterium, which is transmitted through black-legged (deer) tick bites. This study presents a novel hybrid deep learning framework for classifying Lyme disease rashes, utilizing pre-trained models (ResNet50 V2, VGG19, DenseNet201) for initial classification. By combining VGG19 and DenseNet201 architectures, we developed a hybrid model, SkinVisualNet, which achieved an impressive accuracy of 98.83%, precision of 98.45%, recall of 99.09%, and an F1 score of 98.76%. To ensure the robustness and generalizability of the model, 5-fold cross-validation (CV) was performed, generating an average validation accuracy between 98.20% and 98.92%. Incorporating image preprocessing techniques such as gamma correction, contrast stretching and data augmentation led to a 10–13% improvement in model accuracy, significantly enhancing its ability to generalize across various conditions and improving overall performance. To improve model interpretability, we applied Explainable AI methods like LIME, Grad-CAM, CAM++, Score CAM and Smooth Grad to visualize the rash image regions most influential in classification. These techniques enhance both diagnostic transparency and model reliability, helping clinicians better understand the diagnostic decisions. The proposed framework demonstrates a significant advancement in automated Lyme disease detection, providing a robust and explainable AI-based diagnostic tool that can aid clinicians in improving patient outcomes. | |
| 653 | |a Medical diagnosis | ||
| 653 | |a Infectious diseases | ||
| 653 | |a Accuracy | ||
| 653 | |a Arachnids | ||
| 653 | |a Data augmentation | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Erythema | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Neural networks | ||
| 653 | |a Medical imaging | ||
| 653 | |a Skin diseases | ||
| 653 | |a Machine learning | ||
| 653 | |a Explainable artificial intelligence | ||
| 653 | |a Global health | ||
| 653 | |a Lyme disease | ||
| 653 | |a Bacteria | ||
| 700 | 1 | |a Turjy Rittik Chandra Das |u Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh; amir.cse@diu.edu.bd (A.S.); turjy15-6289@s.diu.edu.bd (R.C.D.T.); bappy15-6155@s.diu.edu.bd (S.P.B.); assaduzzaman.cse@diu.edu.bd (M.A.) | |
| 700 | 1 | |a Bappy, Sarbajit Paul |u Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh; amir.cse@diu.edu.bd (A.S.); turjy15-6289@s.diu.edu.bd (R.C.D.T.); bappy15-6155@s.diu.edu.bd (S.P.B.); assaduzzaman.cse@diu.edu.bd (M.A.) | |
| 700 | 1 | |a Assaduzzaman Md |u Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh; amir.cse@diu.edu.bd (A.S.); turjy15-6289@s.diu.edu.bd (R.C.D.T.); bappy15-6155@s.diu.edu.bd (S.P.B.); assaduzzaman.cse@diu.edu.bd (M.A.) | |
| 700 | 1 | |a Marouf, Ahmed Al |u Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada; rokne@ucalgary.ca (J.G.R.); alhajj@ucalgary.ca (R.A.) | |
| 700 | 1 | |a Rokne Jon George |u Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada; rokne@ucalgary.ca (J.G.R.); alhajj@ucalgary.ca (R.A.) | |
| 700 | 1 | |a Alhajj Reda |u Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada; rokne@ucalgary.ca (J.G.R.); alhajj@ucalgary.ca (R.A.) | |
| 773 | 0 | |t Machine Learning and Knowledge Extraction |g vol. 7, no. 4 (2025), p. 157-184 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286316558/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286316558/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286316558/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |