Paper Title
Skin Cancer Classification Using Deep Learning

Abstract
Skin cancer is a significant public health concern, with millions of new cases reported worldwide each year. Early and accurate diagnosis is crucial for effective treatment and improved patient outcomes. Deep learning techniques have shown great promise in automating the detection and classification of skin cancer, reducing the reliance on human expertise. This study presents a deep learning-based approach for the classification of skin cancer using a convolutional neural network (CNN). A large dataset of dermatoscopic images is employed to train and evaluate the model, comprising various skin cancer types, including melanoma. The proposed model leverages the power of deep learning to automatically extract relevant features from skin lesion images, enabling it to discriminate between benign and malignant cases with high accuracy. The model's performance is assessed using various evaluation metrics, such as sensitivity, specificity, and overall accuracy, demonstrating its effectiveness in distinguishing between different skin cancer types. Keywords -Federated Learning, Neural Networks, SocketProgramming, Flower Client and Server.