## 8 Comparative Analysis of Neural Network Architectures and Datasets in Skin Diagnostics
### Comparison of Datasets  
In the literature on dermoscopic skin images, three large datasets are often used: ISIC, HAM10000, and BCN20000. Below, we compare these datasets in terms of the number of images, the number of skin disease classes, the most commonly used evaluation metrics, and the main limitations of each, based on information from publications describing these datasets. **ISIC** (e.g., ISIC 2019 Challenge Dataset). This dataset is the largest of those compared--it contains 25,331 training images (plus 8239 test images) grouped into eight diagnostic classes of skin dermatoses [223]. These classes include, among others, melanoma, melanocytic nevus, basal cell carcinoma (BCC), actinic keratosis (AK), seborrheic keratoacanthoma (BKL), dermatofibroma (DF), vascular lesions (VASC), and squamous cell carcinoma (SCC). In addition, a ninth class of out-of-distribution images (images that do not \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Architecture** & **Source** & **Dataset** & **Accuracy [\%]** & **AUC** & **Sensitivity [\%]** & **Specificity [\%]** \\ \hline \multirow{3}{*}{Separable Vision Transformer} & \multirow{3}{*}{[213]} & PH2 + ISBI-2017 + & \multirow{3}{*}{95.6} & \multirow{3}{*}{0.95} & \multirow{3}{*}{96.7} & \multirow{3}{*}{95.00} \\  & & HAM10000 + ISIC & & & & \\  & & (9 classes) & & & & \\ \hline \multirow{3}{*}{DSCC\_Net} & \multirow{3}{*}{[214]} & ISIC-2020 + & \multirow{3}{*}{94.17} & \multirow{3}{*}{0.9943} & \multirow{3}{*}{93.76} & \multirow{3}{*}{—} \\  & & HAM10000 + & & & & \\  & & DermIS (4 classes) & & & & \\ \hline Swin Transformer & [215] & (2022) & ISIC-2019 (8 classes) & 97.20 & — & 82.30 & 97.90 \\ \hline GoogleNet-TL & [216] & (2020) & ISIC-2019 (8 classes) & 94.92 & — & 79.80 & 97.00 \\ \hline \multirow{3}{*}{SkinNet} & \multirow{3}{*}{[217]} & HAM10000 & \multirow{3}{*}{86.70} & \multirow{3}{*}{0.96} & \multirow{3}{*}{—} & \multirow{3}{*}{—} \\  & & HAM10000 & & & & \\ \cline{1-1}  & & (7 classes) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the quality of selected neural network architectures in the diagnosis of skin diseases (2019–2025). fit into any of the eight categories) was added to the test set [223; 224]. The balanced multi-class accuracy is the most commonly reported metric when evaluating model performance on ISIC data, and it was the primary metric in ISIC competitions [225]. The ISIC dataset suffers from a strong class imbalance, e.g., the vast majority of images are nevus lesions, while some classes (e.g., melanomas and dermatofibromas) make up a small percentage of the data [43; 223]. This can cause models to underperform for rare categories if appropriate balancing techniques are not applied [223]. Another challenge is the limited variety of skin tones captured in these data--reviews have shown that public dermoscopic image collections are underrepresentative of darker skin types [226]. This results in reduced AI model performance for darker skin types, as models are trained primarily on light skin types. Another limitation is the partial lack of histopathological confirmation for some images in ISIC [223]. **HAM10000**. This is a set of 10,015 dermoscopic images initially provided as a training set in the ISIC 2018 competition [43; 223]. It includes seven classes of skin lesions most frequently encountered in dermatological practice, i.e., melanoma, melanocytic nevus, basal cell carcinoma (BCC), actinic keratosis/Bowen's disease (AKIEC), benign keratotic lesions (BKL), dermatofibroma, and vascular lesions [43]. The most common metric in assessing effectiveness here is the balanced classification accuracy. Due to the uneven number of individual categories, it is recommended to weight the results so that each class has an equal impact [225]. The HAM10000 set, like ISIC, has a class imbalance--images of nevi dominate, while, for example, dermatofibromas or vascular lesions constitute a miniscule fraction [43].