# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 5 Discussion  
In our study, we aimed to create a model that fully incorporates the ABCD rule. However, we were unable to measure the D (diameter) of the lesions because the images in the HAM10000 dataset were collected under inconsistent conditions, with varying degrees of angles, distances, and magnifications. To overcome these challenges, one possible improvement would be to collect a dataset where lesions are photographed under standardized conditions, ensuring consistent angles, distances, and lighting. This would allow the model to better utilize diameter information, a critical aspect for skin lesion diagnosis, especially  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Weights** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline ABC ensemble & [1, 1, 1, 1] & 0.9313 & 0.9304 & 0.9313 & 0.9294 \\ \hline ABC ensemble & [2, 1, 1, 1, 1] & 0.9320 & 0.9311 & 0.9320 & 0.9302 \\ ABC ensemble & [1, 2, 1, 1, 1] & 0.9315 & 0.9307  
given that lesion size is an important feature in melanoma classification. We expect that integrating diameter as a learnable feature could potentially improve diagnostic accuracy.  
In the HAM10000 dataset, certain skin lesion types, such as nevi (nv), are overrepresented compared to rarer lesion types like melanoma (mel) or dermatofibroma (df). This imbalance can lead to the model becoming biased toward the more frequent classes, reducing its sensitivity to detecting the rarer yet clinically significant lesions like melanoma. To mitigate this issue, we applied classic augmentation techniques such as rotation, zoom, horizontal flip, and vertical flip, as described in Section 3.1. However, the excessive oversampling of minority classes using basic augmentation methods could result in repetitive data, which might negatively affect the model by introducing redundancy and overfitting. A more advanced approach, such as incorporating Generative Adversarial Networks (GANs), could be a potential solution. GANs can generate synthetic but diverse images for minor classes, helping the model to learn better without relying on repeated data. Therefore, the model would be exposed to a broader set of lesion features, reducing overfitting and improving generalization.  
Another limitation of our study is the lack of data. When the dataset is insufficient, the model may overfit to the training data, leading to poor generalization performance, or it may fail to adequately learn the necessary patterns and features. As shown in Section 4.2, the performance of the 'akic' class was not as high as desired, likely due to the insufficient learning of the lesion characteristics caused by the limited data available for this class. This is further supported by the observation that the 'nv' class, which has the most images, achieved noticeably better classification performance compared to the other classes. To address this issue, future studies should aim to collect and integrate additional datasets to improve the model's predictive performance. Especially in medical applications, the demands for high performance are critical because of the serious implications that incorrect classifications can have. Although our model has shown better classification performance compared to several other models in this study, we acknowledge that there is still room for improvement. Therefore, at the current stage, we think that our model is more suitable as an auxiliary diagnostic tool for medical professionals, rather than as a standalone diagnostic system. However, by expanding the dataset, training the model for more epochs, increasing the current 5-fold cross-validation to 10-fold, and exploring improved methodologies, we expect that we can further enhance the model's classification accuracy and reliability. The ultimate goal will be to reach a level of performance that instills sufficient clinical confidence for the model to be used as an independent diagnostic tool.