Objectives: Now a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts’ experience which may lead to misdiagnosis and mistreatment plans. This study aims to develop a system for the automatic diagnosis of skin cancer margin for squamous cell carcinoma from histopathology microscopic images by applying deep learning techniques. Methods: The system was trained, validated, and tested using histopathology images of SCC cancer locally acquired from Jimma Medical Center Pathology Department from seven different skin sites using an Olympus digital microscope. All images were preprocessed and trained with transfer learning pre-trained models by fine-tuning the hyper-parameter of the selected models. Results: The overall best training accuracy of the models become 95.3%, 97.1%, 89.8%, and 89.9% on EffecientNetB0, MobileNetv2, ResNet50, VGG16 respectively. In addition to this, the best validation accuracy of the models was 94.7%, 91.8%, 87.8%, and 86.7% respectively. The best testing accuracy of the models at the same epoch was 95.2%, 91.5%, 87%, and 85.5% respectively. From these models, EfficientNetB0 showed the best average training and testing accuracy than the other models. Conclusions: The system assists the pathologist during the margin assessment of SCC by decreasing the diagnosis time from an average of 25 minutes to less than a minute.

Background/Objectives: Squamous cell carcinoma (SCC), a prevalent form of skin cancer, presents diagnostic challenges, particularly in resource-limited settings with a low-quality imaging infrastructure. The accurate classification of SCC margins is essential to guide effective surgical interventions and reduce recurrence rates. This study proposes a vision transformer (ViT)-based model to improve SCC margin classification by addressing the limitations of convolutional neural networks (CNNs) in analyzing low-quality histopathological images. Methods: This study introduced a transfer learning approach using a ViT architecture customized with additional flattening, batch normalization, and dense layers to enhance its capability for SCC margin classification. A performance evaluation was conducted using machine learning metrics averaged over five-fold cross-validation and comparisons were made with the leading CNN models. Ablation studies have explored the effects of architectural configuration on model performance. Results: The ViT-based model achieved superior SCC margin classification with 0.928 ± 0.027 accuracy and 0.927 ± 0.028 AUC, surpassing the highest performing CNN model, InceptionV3 (accuracy: 0.86 ± 0.049; AUC: 0.837 ± 0.029), demonstrating robustness of ViT over CNN for low-quality histopathological images. Ablation studies have reinforced the importance of tailored architectural configurations for enhancing diagnostic performance. Conclusions: This study underscores the transformative potential of ViTs in histopathological analysis, especially in resource-limited settings. By enhancing diagnostic accuracy and reducing dependence on high-quality imaging and specialized expertise, it presents a scalable solution for global cancer diagnostics. Future research should prioritize optimizing ViTs for such environments and broadening their clinical applications.

Background/Objective: The global spread of Monkeypox (Mpox) has highlighted the urgent need for rapid, accurate diagnostic tools. Traditional methods like polymerase chain reaction (PCR) are resource-intensive, while skin image-based detection offers a promising alternative. This study evaluates the effectiveness of vision transformers (ViTs) for automated Mpox detection. Methods: By fine-tuning a pre-trained ViT model on an Mpox lesion image dataset, a robust ViT-based transfer learning (TL) model was created. Performance was assessed relative to convolutional neural network (CNN)-based TL models and ViT models trained from scratch across key metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Furthermore, a transferability measure was utilized to assess the effectiveness of feature transfer to Mpox images. Results: The results show that the ViT model outperformed a CNN, achieving an AUC of 0.948 and an accuracy of 0.942 with a p-value of less than 0.05 across all metrics, highlighting its potential for accurate and scalable Mpox detection. Moreover, the ViT models yielded a better hypothesis margin-based transferability measure, highlighting its effectiveness in transferring useful learning weights to Mpox images. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations also confirmed that the ViT model attends to clinically relevant features, supporting its interpretability and reliability for diagnostic use. Conclusions: The results from this study suggest that ViT offers superior accuracy, making it a valuable tool for Mpox early detection in field settings, especially where conventional diagnostics are limited. This approach could support faster outbreak response and improved resource allocation in public health systems.