Aug 2026· The Scientist· Vol 8, pp. 195· 0 citations· 39 references
TL;DR
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.
Abstract
Diagnosing skin diseases remains a clinical challenge due to the visual similarity among diverse dermatological conditions. This study presents a prototype deep learning–powered system for multi-class dermatological screening, implemented through a cloud-based architecture and accessed via a smartphone interface. A pre-trained Convolutional Neural Network (CNN), EfficientNetV2B3, was fine-tuned on a composite dataset encompassing nine disease categories. The model achieved promising performance, with an accuracy of 0.87, precision of 0.87, recall of 0.87, and an F1 score of 0.86, indicating its potential reliability for automated classification. Prototype validation was conducted using a cloud API (Google Cloud Storage + PostMan) to verify the inference pipeline and user interaction. While the current implementation demonstrates the feasibility of cloud-based dermatological screening, real-device mobile performance metrics such as latency, model size, and memory consumption remain future work. Users can capture or upload skin images, which are processed to generate preliminary diagnostic feedback, including symptom descriptions and general treatment information. While not intended to replace professional medical evaluation, the prototype serves as a proof-of-concept tool for initial screening and early intervention. This work illustrates how artificial intelligence (AI) can be harnessed in mobile health applications to expand access to dermatological care and supports broader initiatives to integrate AI into healthcare delivery.
Viral skin infections remain a significant public health concern, particularly in resource-limited regions where access to dermatological expertise is constrained. This study presents a deep learning–based system for automated detection of viral and related skin diseases using image classification and segmentation techniques. A ResNet-152 convolutional neural network was fine-tuned using the FastAI framework and trained on an augmented dataset of 3,028 images derived from 703 original samples across five conditions: monkeypox, chickenpox, warts, eczema, and corns. Data preprocessing and augmentation techniques were applied to address class imbalance and improve generalization. The proposed model achieved an overall classification accuracy of 92%, with notable improvements observed in underrepresented classes such as eczema after augmentation. Unlike prior studies primarily focused on skin cancer or single-disease classification, this work emphasizes viral skin infections and integrates the trained model into a real-time Flask-based web application for practical deployment. The results demonstrate the effectiveness of deep residual networks in multi-class dermatological classification and highlight the potential of AI-driven tools for accessible and early skin disease screening.
Genevieve A. Pilongo, Christopher Josh L. Dellosa, Andre Miguel C. Bacaling et al.· International Conference on...· 0 citations
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
Muhammad Akhdaan, Majid Rahardi· Matrix: Jurnal Manajemen Tek...· 0 citations
Skin disorders affect approximately 1.9 billion people worldwide and remain significantly underdiagnosed due to the global shortage of dermatologists. This study provides a thorough comparative investigation between four pre-trained deep CNN models — ResNet-50, EfficientNetB0, MobileNetV2, and Xception — for automatic classification of five important skin disorders: Acne, Actinic Keratosis, Basal Cell Carcinoma, Eczema, and Rosacea. A balanced data collection comprising 625 images was curated from publicly accessible dermatological databases and split into training, validation, and test sets (375/65/185) to allow proper early stopping and learning-rate scheduling during fine-tuning. All models used architecture-specific preprocessing, with Xception trained at its native 299×299 input resolution while the remaining three models used 224×224. Accuracy, precision, recall, F1 score, per-class F1, AUC-ROC and confusion matrices were used as evaluation criteria. Gradient-Weighted Class Activation Maps (Grad-CAM) were computed on all four models to provide visual interpretability, highlighting the image regions that most influenced each prediction. EfficientNetB0 attains the maximum accuracy (71.89%) and macro F1 score (0.72), followed closely by Xception (68.11% accuracy) and ResNet-50 (67.57% accuracy), while also achieving the highest macro-average AUC-ROC (0.926). MobileNetV2 records the lowest overall accuracy (64.86%) but remains attractive for low-resource deployment given its lightweight architecture. A dataset-size suitability analysis also sheds light on the optimal model choice depending on the amount of data available.
Vishwajeet Lad, Pradnya V. Kulkarni· 2026 International Conferenc...· 0 citations
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.
Mithila Yeasmin Mitu· American Journal of Innovati...· 0 citations
BACKGROUND
Skin cancer is one of the most common malignancies worldwide, and early detection is essential for improving treatment outcomes and reducing mortality. Conventional diagnostic approaches rely on visual examination and dermoscopic analysis by dermatologists, which can be time-consuming and subject to inter-observer variability. Recent advances in artificial intelligence have enabled the development of computer-aided diagnostic systems to support clinical decision-making in dermatological oncology.
OBJECTIVE
In this study, a deep learning-based framework is proposed for multi-class classification of cutaneous lesions using dermoscopic images.
METHODS
The proposed model integrates a Vision Transformer (ViT) to capture global contextual features and a Squeeze-and-Excitation Residual Network (SE-ResNet) to extract channel-refined local features. These complementary representations are combined using an adaptive attention-based fusion mechanism to improve classification performance. In addition, an Improved Crocodile Optimization Algorithm (ICOA) is employed to optimize model hyperparameters and enhance convergence stability.
RESULTS
The proposed method was evaluated using the HAM10000, ISIC 2019, and PH2 datasets, achieving classification accuracies of 99.05%, 98.31%, and 99.17%, respectively.
CONCLUSION
The results demonstrate the robustness and generalizability of the proposed framework, highlighting its potential as a clinical decision-support tool for early detection and improved diagnosis of skin cancer.
S. Ravisankar, M. Karthikeyan· Cutaneous and Ocular Toxicol...· 0 citations
Skin diseases affecting the body area represent a significant dermatological challenge due to the high visual similarity between conditions, which complicates accurate diagnosis. Prior studies have predominantly relied on pure CNN architectures, which are inherently limited in capturing long-range contextual relationships between distant lesion regions, and no existing work has integrated CNN with Vision Transformer specifically for body area skin disease classification. This study addresses this gap by applying the SEMMA (Sample, Explore, Modify, Model, Assess) methodology to develop a novel hybrid classification model integrating Convolutional Neural Network (CNN) and Vision Transformer (ViT) for classifying four types of skin diseases on the body area, namely Acne and Rosacea, Eczema, Psoriasis, and Tinea Ringworm and Fungal Infections, using the DermNet dataset. Two CNN backbones were evaluated: DenseNet201 and EfficientNetB4, each combined with four Pre-LayerNorm Transformer Blocks that feature learnable positional encoding to capture long-range spatial dependencies among feature tokens. A two-phase training strategy was implemented, consisting of feature extraction followed by fine-tuning, with balanced class weight applied to address class imbalance detected during exploratory data analysis. The original training data was split into training and validation sets at a 90:10 ratio, while the test data was sourced from the built-in test folder of the DermNet repository, yielding 4,301 training images, 479 validation images, and 1,298 test images. Evaluation on fully isolated test data showed that DenseNet201+ViT achieved 78% accuracy and EfficientNetB4+ViT achieved 77% accuracy. The application of the probability-averaging ensemble strategy further improved performance to 82% accuracy and a Macro F1-Score of 82%, surpassing prior CNN-based studies in the same domain. Grad-CAM visualization confirmed that the model focused attention on clinically meaningful lesion areas, indicating that its predictions are grounded in relevant morphological features rather than image artifacts. However, this study is limited by the absence of clinical validation on real patient data, and the current accuracy remains below the threshold required for standalone diagnostic use. The findings demonstrate that hybrid CNN+ViT architectures, combined with ensemble strategies, offer a promising and interpretable approach for automated skin disease classification, warranting further clinical validation before deployment as a diagnostic support tool.
Yogi Sugiman, M. Daniswara· Journal of Intelligent Syste...· 0 citations