A Novel End-to-End Deep Learning Approach for Skin Cancer Detection Based on Web Application

Skin cancer is a common, potentially fatal condition that requires early detection for successful treatment.Many cancerous cases are diagnosed in the advanced stages, which makes the chances of recovery very small, resulting in the inability to provide appropriate treatment promptly.This includes skin cancer, which causes complete damage to the affected area until it reaches the deepest layers.Previous studies have developed systems based on the diagnosis of this disease with the help of deep learning (DL), which can detect cancer in its early stages.In this study, using the Kaggle Melanoma Skin Cancer Dataset of 10000 Images dataset, which consists of over 10,000 high-quality skin lesion images, we present a novel DL method for skin cancer detection based on the DensNet121 model.Several alternative models, including DensNet121 + XGBoost Classifier, a dedicated Convolutional Neural Network (CNN) model, an ensemble model based on DensNet121, the Enhancing CNN model, and ResNet50, were also designed, implemented, and tested in addition to our main model, DensNet121.With regard to accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC), our proposed model showed promising results after undergoing thorough evaluation and comparison with other recognized models.The DensNet121-based model demonstrated astounding 98% training accuracy, demonstrating its effectiveness in learning from training data.It kept up an admirable 82% validation accuracy, demonstrating its capability to handle new cases.The test's 78% accuracy rate proved that it worked well in practical situations.All three metrics-recall, precision, and F1 score-met exceptional benchmarks of 98%, demonstrating the model's prowess at identifying true positive cases and reducing false positives.Furthermore, there was 97% of the MCC indicating the high degree of accuracy between the forecast and the outcome.In the comparison analysis our model was better than ensemble model and conventional convolutional neural networks, indicating the importance of high training accuracy and generalization.