Over the past decade, artificial intelligence (AI) has significantly transformed the field of healthcare due to its ability to analyze large volumes of clinical data and assist medical professionals in decision-making. ¹ In dermatology, this transformation has been particularly relevant, given that the diagnosis of skin diseases depends largely on visual observation and the experience of the specialist, which can lead to diagnostic variability and delays in timely treatment. ² In this context, Convolutional Neural Networks (CNNs) have established themselves as one of the most promising tools for the automated classification of medical images. ³

At the same time, artificial intelligence has also been successfully applied in other domains through the development of expert systems for assisted decision-making. For example, Huayta-Gómez and Pacheco ⁴ implemented an expert system for vocational guidance, structured around a six-model approach—organizational, task and agent, knowledge, communication, design, and implementation—that optimized the process of diagnosis and self-knowledge among students. This type of methodological approach demonstrates the versatility of artificial intelligence in the design of interactive and reproducible solutions, principles that also guide the development of the present study in the context of assisted dermatological diagnosis.

Several studies have explored the application of deep learning models to support dermatological diagnosis. For example, Kadhim and Kamil ⁵ developed a machine learning-based system for the classification of dermatological images, optimizing the clinical analysis process. Similarly, Lesaunier et al. ⁶ and Zhang et al. ⁷ showed that the incorporation of information technology systems in areas such as interventional radiology improves efficiency and reduces errors through the application of Lean principles. ⁸ These advances demonstrate the potential of AI to transform medical diagnosis, although they also highlight limitations related to the accessibility and reproducibility of the tools developed.

CNNs, in particular, reproduce the human visual learning process through convolutional layers that allow complex patterns in images to be identified. ⁹ Their application has spread to specialties such as radiology, digital pathology, and dermatology, achieving results comparable to and even superior to those of dermatologists in specific classification tasks. ¹⁰ In Asia, ¹¹ demonstrated the effectiveness of deep architectures—including CNN, RNN, GAN, and LSTM—in improving real-time diagnostic accuracy. As shown in Figure 1, these types of networks have transformed the diagnostic process by complementing clinical evaluation with deep learning–based decision support systems. However, most of these developments are concentrated in environments with high technological availability and large databases, which limits their adoption in developing countries.

In Latin America, the adoption of AI-based tools has advanced gradually. Tentori et al. ¹² reported the development of telemedicine and computer-assisted diagnosis initiatives in Brazil and Mexico, showing promising results despite persistent challenges related to technological infrastructure and access to standardized clinical data. In Peru, studies by Ponce et al. ¹³ and Sarria et al. ¹⁴ highlighted advances in medical automation, yet revealed the lack of local solutions focused on dermatological image analysis using deep learning models. This gap emphasizes the need for accessible and reproducible tools designed for academic research and clinical integration of artificial intelligence.

In response to this need, DERMOSAN, an interactive software tool in Streamlit for CNN-assisted dermatological diagnosis, was developed. Its purpose is to offer an intuitive development phase platform that allows dermatological images to be uploaded and probabilistic diagnoses to be obtained in real time. Unlike previous solutions, DERMOSAN integrates an advanced CNN architecture (ResNet152) with an interactive open-source interface, facilitating its use for academic, research, and demonstration purposes, promoting reproducibility and support for medical specialists.

In this section, we describe the study design, the origin of the data, the technical implementation of the model, and the operation of the assisted dermatological diagnosis tool.

The model developed achieved an accuracy of 95% in training, 83% in validation, and 92.3% in testing, reflecting the technical performance achieved in the development stage of the DERMOSAN software. These values indicate solid model performance, with slight differences between training and validation attributable to class imbalance.

Figure 4 shows the confusion matrix obtained in the test set. Superior performance is observed in classes with a greater number of images, such as Melanocytic Nevi (NV) and Basal Cell Carcinoma (BCC), while minority categories (Eczema, Atopic Dermatitis) show more classification errors.

Figure 5 shows the evolution of loss during training and validation. A progressive reduction is observed in the training set, while in validation the values remain relatively high, indicating the presence of overfitting.

Figure 6 shows the accuracy curve. The model achieved over 95% accuracy in training, while in validation it stabilized at around 83%. This difference is related to the difficulty of adequately generalizing in classes with lower representativeness.

Figure 7 shows the graphical interface developed in Streamlit, which allows individual images to be uploaded and the model’s suggested diagnosis to be obtained in real time. The application displays the main diagnosis with its confidence level, as well as the most likely differential diagnoses.

Finally, the overall performance of the model on the test set is shown in Figure 8, where a total accuracy of 92.3% was achieved when evaluating the 2,723 test images. This result confirms the model’s ability to generalize on unseen data, maintaining a consistent relationship with the training and validation metrics.

Although additional metrics such as precision, recall, or F1-score were not calculated, their inclusion is recommended in future work to evaluate the balance of the model, especially in minority classes. During training, it was observed that the differences in performance between training and validation remained consistent from the early epochs, suggesting that the model learned quickly and that class imbalance limits its generalization ability in less represented classes.

To evaluate the practical functioning of the developed system, controlled tests were performed in the local version of the Streamlit environment. These tests simulated real clinical use scenarios, with the aim of verifying the model’s ability to process dermatological images, generate diagnoses, and issue interpretive recommendations.

The implementation of the CNN-based model (ResNet152 with transfer learning) demonstrated a notable improvement in the automatic classification of dermatological conditions, achieving accuracies of 95% for training, 83% for validation, and 92.3% for testing. These results indicate that deep learning models can effectively complement dermatological diagnosis by reducing the variability associated with human clinical judgment. The obtained performance aligns with that reported by Liu et al., ¹⁶ who developed a multi-classifier architecture for skin lesion recognition using multi-scale attention mechanisms, achieving an accuracy of 88.2% on the HAM10000 dataset. Similarly, Musthafa et al. ¹⁷ achieved 97.78% using an optimized CNN, while Shetty et al. ¹⁸ reported 95.18% with a cross-validation strategy, reinforcing the effectiveness of deep convolutional architectures combined with data augmentation techniques.

The proposed model performed better in classes with higher representativeness, such as Melanocytic Nevi and Basal Cell Carcinoma, which is consistent with the findings of Winkler et al., ¹⁹ who demonstrated that CNNs can achieve diagnostic sensitivities comparable to those of dermatologists. This trend has been repeatedly observed in dermatology, where CNN-based models have increased the diagnostic accuracy of specialists, supporting their value as clinical decision-support tools. However, minority classes such as Eczema and Atopic Dermatitis showed higher error rates, confirming the negative impact of data imbalance on model generalization—an issue also highlighted in the systematic reviews of Brinker et al. ²⁰

The use of the class_weight parameter partially mitigated the imbalance effects, improving training stability and reducing overfitting. This approach is consistent with that of Shetty et al., ¹⁸ who emphasized the importance of rebalancing strategies to enhance model robustness. In addition, integrating a Streamlit-based graphical interface enabled the model to be deployed in an interactive, accessible, and reproducible environment, facilitating real-time image analysis and the visualization of results with interpretable confidence levels. This development aligns with the recommendations of Esteva et al., ²¹ who underscored the need to adapt CNN-based diagnostic systems to everyday clinical workflows.

The visual outcomes obtained through the confusion matrix and training curves indicated stable and predictable performance, with deviations primarily attributable to the dataset composition. These findings are consistent with those of Liu et al., ¹⁶ who demonstrated that biases in data distribution directly affect validation metrics. Overall, the proposed tool demonstrates the feasibility of combining deep learning and accessible visual environments to support dermatological diagnosis.

The main limitations of this study include class imbalance, dependence on a single public dataset, the absence of complementary evaluation metrics such as precision, recall, and F1-score, and the lack of direct clinical validation with real cases.

For future work, we propose expanding the dataset with local samples, performing multicenter external validations, and exploring explainable AI (XAI) approaches to improve model transparency and clinician trust in real-world applications.

This study confirmed the effectiveness of convolutional neural networks (CNNs)—specifically the ResNet152 architecture with transfer learning—as a support tool for the automated diagnosis of dermatological conditions. The model achieved accuracies of 95% in training, 83% in validation, and 92.3% in testing, demonstrating strong classification performance and reproducibility under controlled conditions.

The integration of a Streamlit-based interface enabled the model to be deployed in a practical and interactive environment, facilitating its use by professionals and its potential adaptation to clinical workflows. This development demonstrates the feasibility of linking deep learning models with accessible visual applications, enhancing the interpretability of results and supporting medical decision-making.

Nevertheless, the study has limitations related to class imbalance and reliance on a single public dataset, which may affect the generalization capability of the model in broader clinical contexts. Future research should address these limitations by incorporating more representative datasets, conducting multicenter external validations, and incorporating additional evaluation metrics such as precision, recall, and F1-score for a more comprehensive assessment of model performance.

Overall, this research represents an initial step toward the practical integration of artificial intelligence in dermatology, underscoring the potential of CNN-based tools to complement clinical diagnosis. Under professional supervision, such systems could significantly contribute to improving diagnostic accuracy and efficiency in dermatological care.

Software available from: ---

Source code available from: https://github.com/tavoofg/dermosan-dermatology-cnn

Archived source code at time of publication: https://doi.org/10.5281/zenodo.17281102 ²³

License:* https://opensource.org/license/MIT