# Enhanced skin cancer classification using modified efficientNetV2L with adaptive early stopping mechanism

## TL;DR

A new deep learning model for skin cancer classification improves accuracy and generalization using modified EfficientNetV2L with adaptive early stopping.

## Contribution

A novel skin cancer classification model using modified EfficientNetV2L with adaptive early stopping to prevent overfitting and improve generalization.

## Key findings

- The model achieved 99.22% classification accuracy on the ISIC dataset.
- Adaptive early stopping and learning rate callbacks improved generalization and reduced overfitting.
- The model demonstrates robust performance across diverse skin lesion types.

## Abstract

The accurate classification of skin cancer types is a critical task in medical diagnostics, requiring robust and reliable models to distinguish between various skin lesions. Despite advancements in deep learning, developing models that generalize well to unseen data remains a challenge. Current methodologies primarily utilize convolutional neural networks (CNNs) for image classification tasks, leveraging architectures such as ResNet, VGG, and Inception. These models have shown promise in improving classification accuracy for skin cancer detection. However, existing models often face limitations, including overfitting to the training data and difficulty in handling imbalanced datasets. This results in decreased performance on validation and test datasets, reducing their practical applicability in clinical settings. Additionally, these models may lack the fine-grained discrimination required to accurately classify a diverse range of skin lesion types. To address the limitations of traditional CNN-based approaches, we propose a novel model based on the EfficientNetV2L architecture, optimized for skin lesion classification. Our approach introduces adaptive early stopping and learning rate callbacks to enhance generalization and prevent overfitting. Trained on the ISIC dataset, the model achieved a high classification accuracy of 99.22%, demonstrating robustness across various lesion types. This work contributes a powerful, efficient, and clinically relevant solution to the field of automated skin cancer diagnosis.