Skin Disease Detection
The goal of the Skin Disease Detection project is to create a computer-aided diagnostic system for the early identification and categorization of skin illnesses. The system will accept photos of skin lesions as input and preprocess the images using image processing algorithms in order to do additional analysis. To classify the input images into various types of skin illnesses, convolutional neural networks (CNNs) will be trained on a dataset of skin lesion images. Additionally, transfer learning strategies will be employed to enhance the precision of pre-trained CNN models. Techniques for data augmentation will be utilized to produce more training data and enhance model performance. The system will be installed, and a web-based user interface will be provided for convenience. The research intends to increase skin disease early detection, which can improve patient outcomes and lower healthcare expenditures.
Keywords: Skin Disease classification, computer-aided diagnosis, Image Processing, Machine Learning, Convolutional Neural Networks (CNNs), Transfer Learning, Data Augmentation.
Tools: Python, Visual Studio Code, Flask, Convolutional Neural Networks, Image Processing, Numpy, Matplotlib, TensorFlow
Department: Department of Computer Science
Project Team Members
| Name |
Email |
|
Fareeha Bano
|
fareeha2019@namal.edu.pk |
|
Atiqa Nisa
|
atiqa2019@namal.edu.pk |
Project Poster
