## 2 Related Work  
The development and application of machine learning and convolutional neural network (CNN) models for skin lesion classification have seen significant advancements in recent years. Initially, traditional machine learning techniques were widely employed in skin lesion classification, utilizing algorithms like Multi-class Support Vector Machines (SVMs) [13] and K-Means clustering [14]. These methods focused on statistical analysis of features extracted manually from images, providing a foundational approach for early classification efforts. As the field progressed, the advent of convolutional neural networks (CNNs) marked a significant shift, with architectures like MobileNetv2 [15] and ResNet-152 [16] offering more sophisticated image analysis capabilities through deep learning, enabling automatic feature extraction directly from raw images.  
Further advancements led to the integration of segmentation techniques alongside CNNs, enhancing the precision of classifications. Methods such as U-Net began to be employed for isolating lesions from surrounding skin before classification, improving the accuracy by focusing the CNNs on relevant image segments only. The culmination of these developments has been the adoption of multi-input CNN models that integrate segmented image data with patient metadata, using diverse architectures to capture a broad spectrum of dermatological features effectively. In the advanced stages, researchers have explored various strategies to optimize the accuracy and efficiency of these models, particularly through the use of multi-input systems that combine different data types and processing techniques. Recent studies have heavily employed CNN architectures due to their efficacy in handling complex image data. For instance, [PERSON] et al. [17] utilized a multi-faceted approach involving segmentation with R2U-Net followed by classification using a range of CNN models including EfficientNetB0-B7, SENet-154, ResNeXt-101 32x4d, and Inception-ResNet-v2. This methodology emphasizes the critical role of precise lesion isolation and the subsequent application of diverse CNN architectures to capture and classify various dermatological features effectively, followed by employing various CNN architectures--yielding an accuracy of 96.86%, re-call of 85%, precision of 82%, and an F1 score of 83%. Many existing models, while achieving high accuracy on specific datasets, struggle to maintain performance across diverse datasets due to overfitting and the highly specialized nature of the training data. This limitation often results in reduced effectiveness when applied to real-world clinical settings where data variability is high.  
The incorporation of multi-input CNN models marks a pivotal advancement in dermatological imaging. These models integrate multiple data streams, such as high-resolution images, extracted regions of interest, and additional contextual information, to enhance diagnostic precision. [PERSON] et al. [18] proposed an end-to-end model that maximizes the use of high-resolution images through a multi-input strategy, including cropping, original image downsampling, and region of interest extraction using CAM, supplemented by an attention mechanism to enhance classification accuracy, achieving an accuracy of 88.4%, recall of 76.7%, and precision of 96.3%. [PERSON] et al. [19] enhanced model performance through transfer learning with a pretrained GoogleNet, fine-tuning the model parameters during training, which resulted in an accuracy of 94.92%, recall of 79.8%, precision of 97%, and an F1 score of 80.36. [PERSON] [20] tackled skin lesion classification by employing an ensemble of deep learning models, including EfficientNet, SENet, and ResNeXt WSL, utilizing multiple input resolutions and dual cropping strategies during training.