Skin Cancer Detection Using Image Classification in Deep Learning

The understanding of deep learning in skin cancer results in a dependable and faster method to diagnose malignant skin lesions at an initial stage. To improve the accuracy of detection, this research uses a number of steps in its methodology. First, we apply the Weiner filter to smooth the input images, minimizing the noise effects and improving the overall image quality. The next step is to use Adaptive Histogram Equalization (AHE) because it enhances the contrast of the image and thus helps in feature extraction. The feature selection process is accomplished through an Autoencoder that decreases dimensionality while preserving essential data characteristics. Finally, classification is executed using a Convolutional Neural Network (CNN), which performs inadequately for pattern recognition in medical images. The model shows a high accuracy of 96.3%, reflecting its effectiveness in diagnosing skin cancer. Such high accuracy proves the effective applicability of the proposed method as a diagnostic aid to dermatologists for timely and efficient reactions. Future works will emphasize external validity and thereby set the system into real practice settings.