## 2 Related Works  
Segmentation, object detection, classification, and picture recognition are all examples of computer vision problems, which frequently use convolutional neural networks (CNNs), a kind of deep learning. In research by [PERSON] et al., the SqueezeNet model obtained 92.18% accuracy in skin cancer classification, suggesting that its lightweight design is acceptable for implementation in resource-limited situations. Its disadvantages include significantly lower accuracy when compared to more complicated models [1]. Similarly, [PERSON] et al. (2024) demonstrated CNNs' ability to diagnose skin illnesses with 94.91% accuracy, highlighting possibilities in telemedicine, early detection, and mobile-based healthcare accessible for rural populations. Despite this, the absence of real-world testing in varied groups raises concerns regarding generalizability [2]. Deep learning advancements have improved methods for detecting skin cancer. Prominent research using the VGG-19 model obtained 97.29% accuracy via transfer learning using pre-trained weights, proving the model's robust feature extraction capability. While this precision is great, the computing demands of the VGG-19 model may make real-time implementation difficult [3]. Other study that leveraged fastai's high-level API focused on constructing CNN models for melanoma diagnosis, obtaining excellent accuracy through data augmentation and hyperparameter tuning. These studies underline fastai's effectiveness for streamlining model construction, although it has disadvantages such as dependency on preset APIs, which may limit customization [4; 5]. Further advancements have been investigated using models such as MobileNetV2 and DenseNet201. In their research, [PERSON] et al. improved these models by using Grad-CAM visualization and extra convolutional layers to increase feature interpretability. Their updated DenseNet201 framework obtained a leading accuracy of 95.50%, demonstrating its efficacy in identifying crucial aspects for diagnosis. However, the model's increasing complexity raises doubts about its appropriateness for application in low-power devices [6]. Preprocessing procedures and data augmentation were also highlighted as critical stages to ensure data quality and model durability, with one suggested deep CNN model achieving 90.48% accuracy. While this strategy is successful, it may be time-consuming for big datasets [7]. The EfficientNet model family has also demonstrated potential in skin cancer diagnosis. [PERSON] et al. found that the EfficientNet B4 model achieved a Top-1 Accuracy of 87.91% and an F1-score of 87%, exhibiting superior performance with fewer parameters than traditional models. However, the lower accuracy shows that there is still space for improvement in feature extraction for complicated scenarios. [PERSON] et al. created an automated approach employing the MobileNet optimizer that achieved 90% accuracy in early skin cancer detection and classification, demonstrating its promise for lightweight, mobile applications [8; 9]. In another CNN-related study, [PERSON] and [PERSON] (2023) built a CNN-based model for skin disease categorization using the Jetson Nano Developer Kit. On the 200-image PH2 dataset, their model surpassed MobileNet (80%) and ResNet50 (56.66%), with a proposed model CNN achieving 93.33% accuracy. Despite this, the dataset's limited size (139 training and 60 test pictures) may restrict its generalizability [10]. A CNN-based model performed admirab in the experiment by [PERSON] and [PERSON], categorizing skin problems into seven distinct groups with 95% accuracy. This highlights the potential for deep learning approaches to automate the diagnosis process, reduce reliance on manual evaluation, and improve clinical decision making in dermatology [11]. In addition, MobileNet, a lightweight convolutional neural network (CNN) which employs depth-wise separable convolutions, has demonstrated potential by obtaining an accuracy of 91.23%.