Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI

• URL: http://arxiv.org/abs/2407.14757v1
• Date: Sat, 20 Jul 2024 05:38:00 GMT
• Title: Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI
• Authors: Jayanth Mohan, Arrun Sivasubramanian, V Sowmya, Ravi Vinayakumar,
• Abstract summary: Skin diseases affect over a third of the global population, yet their impact is often underestimated. Deep learning techniques have shown much promise for various tasks, including dermatological disease identification. This study uses a skin disease dataset with 31 classes and compares it with all versions of Vision Transformers, Swin Transformers and DivoV2.
• Score: 2.3149142745203326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Skin diseases affect over a third of the global population, yet their impact is often underestimated. Automating skin disease classification to assist doctors with their prognosis might be difficult. Nevertheless, due to efficient feature extraction pipelines, deep learning techniques have shown much promise for various tasks, including dermatological disease identification. This study uses a skin disease dataset with 31 classes and compares it with all versions of Vision Transformers, Swin Transformers and DivoV2. The analysis is also extended to compare with benchmark convolution-based architecture presented in the literature. Transfer learning with ImageNet1k weights on the skin disease dataset contributes to a high test accuracy of 96.48\% and an F1-Score of 0.9727 using DinoV2, which is almost a 10\% improvement over this data's current benchmark results. The performance of DinoV2 was also compared for the HAM10000 and Dermnet datasets to test the model's robustness, and the trained model overcomes the benchmark results by a slight margin in test accuracy and in F1-Score on the 23 and 7 class datasets. The results are substantiated using explainable AI frameworks like GradCAM and SHAP, which provide precise image locations to map the disease, assisting dermatologists in early detection, prompt prognosis, and treatment.

Related papers

• Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning [0.0]
  Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. We propose a novel and efficient method for diagnosing skin diseases using deep learning techniques.
  arXiv  Detail & Related papers  (2025-01-23T14:43:53Z)
• An analysis of data variation and bias in image-based dermatological datasets for machine learning classification [2.039829968340841]
  In clinical dermatology, classification models can detect malignant lesions on patients' skin using only RGB images as input. Most learning-based methods employ data acquired from dermoscopic datasets on training, which are large and validated by a gold standard. This work aims to evaluate the gap between dermoscopic and clinical samples and understand how the dataset variations impact training.
  arXiv  Detail & Related papers  (2025-01-15T17:18:46Z)
• An Attention-Guided Deep Learning Approach for Classifying 39 Skin Lesion Types [0.9467360130705921]
  This study advances the field by curating a comprehensive and diverse dataset comprising 39 categories of skin lesions. Five state-of-the-art deep learning models -- MobileNetV2, Xception, InceptionV3, EfficientNetB1, and Vision Transformer - are rigorously evaluated. The Vision Transformer model integrated with CBAM outperforms others, achieving an accuracy of 93.46%, precision of 94%, recall of 93%, F1-score of 93%, and specificity of 93.67%.
  arXiv  Detail & Related papers  (2025-01-10T14:25:01Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Comparative Performance Analysis of Transformer-Based Pre-Trained Models for Detecting Keratoconus Disease [0.0]
  This study compares eight pre-trained CNNs for diagnosing keratoconus, a degenerative eye disease. MobileNetV2 was the best accurate model in identifying keratoconus and normal cases with few misclassifications.
  arXiv  Detail & Related papers  (2024-08-16T20:15:24Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)
• Predictive Analysis of Diabetic Retinopathy with Transfer Learning [0.0]
  This paper studies the performance of CNN architectures for Diabetic Retinopathy Classification with the help of Transfer Learning. The results indicate that Transfer Learning with ImageNet weights using VGG 16 model demonstrates the best classification performance with the best Accuracy of 95%.
  arXiv  Detail & Related papers  (2020-11-08T18:54:57Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.