Skin Cancer Detection using Hybrid Neural Network Model

Skin cancer, the fifth most frequent malignancy worldwide, burdens global health and the economy. Skin cancer rates have increased due to rapid environmental change, indus-trialization, and genetic alteration. This work introduces deep learning on dermatological pictures to identify skin cancer. We gathered 18274 skin photos, both cancerous and not. Scaling, augmenting, and normalizing data provided model robustness. Our skin cancer detection model employs CNNs and LSTMs. We retrieved skin characteristics using the InceptionResNetV2 pre-trained model to enhance discriminative feature learning. Batch normalization and dropout layers prevented overfitting. Our model performed well in training epochs using optimization callbacks. The accuracy was 93.41%, AUC 85.09%, precision 94.03%, and recall 99.10%. However, the model included 211 false positives and 30 negatives. These results show the model can identify skin cancer. These studies show that deep learning systems can diagnose skin cancer. The model’s accuracy and low false positive rate help dermatologists and healthcare providers. This technology may improve skin cancer detection and treat-ment by reducing manual diagnostic subjectivity and accelerating assessments.