Detection Skin Disease using Convolutional Neural Network Model

Abstract

Skin diseases are one of the most important global health problems; thus, early and correct diagnosis is very critical for effective treatment. The following research introduces a Convolutional Neural Network model developed in TensorFlow for classifying skin diseases based on the Skin Cancer MNIST: HAM10000 dataset, a rich collection of dermatoscopic images of pigmented lesions. The goal is to improve diagnostic accuracy and efficiency through automated image classification. The dataset undergoes preprocessing in order to improve the model's generalization ability. Design a CNN model and train it on a large number of images to distinguish different lesion types. Measure its performance based on various metrics, including accuracy, precision, recall, and F1-score. Preliminary results achieved very high accuracy in the classification task, which is an indicative capability for the support model. Future research will be targeted at real-time applications, including the addition of more data to increase coverage. The present study emphasizes the potential role of deep learning in medical diagnostics and provides a useful tool for the automatic recognition of skin diseases, thereby contributing to improved health outcomes.