Deep learning-based detection of viral skin diseases using ResNet-152 with web deployment

Automated Skin Disease Classification Using Deep Neural Networks: A Study on a Bangladeshi Dataset

Skin diseases are a significant public health concern in Bangladesh, with a high prevalence among the population. Accurate and timely diagnosis is essential for effective treatment, but challenges such as limited access to dermatologists in rural areas and the high cost of medical consultations persist. This study proposes a deep learning-based framework for the automated detection of skin diseases in Bangladeshi people, leveraging advanced image processing techniques. Six state-of-the-art deep neural network architectures, DenseNet201, InceptionV3, MobileNet, NASNetLarge, VGG19, and Xception, were trained and evaluated using a dataset curated specifically for the Bangladeshi population. The dataset consisted of dermatologically annotated skin disease images that were preprocessed and augmented to enhance model generalization. Performance evaluation was conducted based on accuracy, precision, recall, and F1 score. Among the tested architectures, NASNetLarge achieved the highest classification accuracy of 91%, demonstrating its potential for reliable skin disease detection. This research provides a robust solution to assist dermatologists, improve healthcare accessibility, and optimize resource allocation in Bangladesh. The developed framework has the potential to be integrated into mobile and web applications for real-time disease detection, thus improving early diagnosis and patient outcomes across the country.

Multi-class skin disease classification using transfer learning architectures

Dermatological conditions are among the most common health problems worldwide, where delayed identification may increase disease severity and complicate treatment procedures. However, restricted access to dermatological expertise and insufficient public awareness often contribute to delayed diagnosis. This study proposes a multi-class skin disease classification approach using deep learning and transfer learning architectures based on digital skin images. The dataset, obtained from the babaruzair/kaggle-skin-disease repository, consists of 1,157 images categorized into eight skin disease classes. Image preprocessing techniques, including resizing, normalization, and data augmentation, were applied to improve data quality and model generalization. Three models were evaluated in this study, namely a baseline Convolutional Neural Network (CNN), MobileNetV2, and EfficientNetB0. Both transfer learning models utilized ImageNet pre-trained weights, followed by customized classification layers and fine-tuning of selected upper layers. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results show that the baseline CNN achieved an accuracy of 52.36%, while MobileNetV2 and EfficientNetB0 achieved accuracies of 88.84% and 95.28%, respectively. The findings demonstrate that transfer learning architectures significantly outperform conventional CNN models for limited medical image datasets, with EfficientNetB0 providing the best classification performance. These results indicate the potential of deep learning-based approaches to support early skin disease diagnosis and assist clinical decision-making

Skin Disease Detection Using Deep Learning

Early and accurate diagnosis of skin diseases is essential for effective treatment and improved patient outcomes. This paper presents SkinScan AI, a deep learning-based framework for automated skin disease detection and classification from dermoscopic images using the HAM10000 benchmark dataset. The proposed DenseNet121 model classifies skin lesions into seven clinically significant categories: Actinic Keratoses (akiec), Basal Cell Carcinoma (bcc), Benign Keratosis-like Lesions (bkl), Dermatofibroma (df), Melanoma (mel), Melanocytic Nevi (nv), and Vascular Lesions (vasc). To address dataset imbalance, the framework incorporates MixUp augmentation, focal loss with label smoothing, minority-class weighting, and oversampling. A two-stage fine-tuning strategy with Test-Time Augmentation (TTA) improves model generalization and prediction robustness. Comparative evaluation with a custom CNN baseline and EfficientNetB3 demonstrates that DenseNet121 achieves the best performance, obtaining 81.82% TTA accuracy, a weighted F1-score of 0.8096, and an AUC of 0.9441. Grad-CAM provides visual explanations by highlighting lesion-relevant regions, improving model interpretability and increasing user confidence in the prediction process. The trained model is deployed as a Flask-based web application with secure authentication, prediction history, severity assessment, medication guidance, doctor referral recommendations, and downloadable PDF reports. Experimental results demonstrate that the proposed framework effectively handles class imbalance while providing accurate, interpretable, and practical decision support for computer-aided skin disease diagnosis. The system offers a scalable and user-friendly solution that can assist healthcare professionals and improve access to reliable preliminary skin disease screening.

Deep Learning-Based Automated Skin Cancer Detection Using Dermoscopic Images

Cloud-Based Deep Learning for Multi-Class Dermatological Screening: An Empirical Study Using Pretrained CNNs

How artificial intelligence can be harnessed in mobile health applications to expand access to dermatological care and supports broader initiatives to integrate AI into healthcare delivery is illustrated.

Deep Learning-Based Skin Cancer Classification Using Hybrid S-ResNet and Explainable AI

Skin cancer is one of the most common and life-threatening diseases in the world. Early detection is essential in order to enhance treatment outcome and patient survival. In this work, a Hybrid S-ResNet framework with Grad-CAM is proposed for automated skin cancer classification using dermoscopic images. In the proposed approach, a modified architecture based on ResNet18 is used as a feature extractor to learn discriminative characteristics of lesions using residual learning. The input images are preprocessed using resizing, normalization and data augmentation techniques to improve the robustness of the model. Weighted Random Sampling, label smoothing and dropout regularization are employed during training to address class imbalance and improve generalization. The model is trained by AdamW optimizer with cosine annealing learning rate scheduling for better convergence and less overfitting. In addition, the proposed framework utilizes Gradient-weighted Class Activation Mapping (Grad-CAM) to produce visual explanations via highlighting the important parts of the image that lead to the prediction. The proposed model was compared with Resnet18, Resnet50, DenseNet121 and EfficientNetB0 under similar experiment settings. Experimental results show that the proposed Hybrid S-ResNet achieved better results with an accuracy of 84.90%, recall of 93.41%, F1-score of 90.69%, and AUC of 90.83%, proving the efficiency of the Hybrid S-ResNet to classify skin cancer. The combination of deep learning and explainable artificial intelligence offers a reliable framework to assist clinical decision making in dermatological diagnosis.