Skin Disease Detection using Machine Learning project on Python

Skin Disease Detection using Machine Learning project abstract

Skin Disease Detection using Machine Learning project description

This project focuses on developing an intelligent system for automated skin disease detection using machine learning. It targets conditions like melanoma, nevus, and basal cell carcinoma. Leveraging image preprocessing and HOG-based feature extraction, the system enhances diagnostic precision. A hybrid classification model combining Naïve Bayes and SVM is implemented for better accuracy. The project primarily aims to assist individuals in remote areas with limited access to dermatologists by providing early and accurate diagnoses. Additionally, the system offers a user-friendly interface to simplify usage for non-technical users, thereby promoting accessible, AI-driven healthcare diagnostics.

Project Modules

The user module offers a clean and intuitive interface for interaction. Users can upload skin lesion images through this module. The uploaded images undergo preprocessing and feature extraction before being classified by the machine learning model. Once processed, the system displays the diagnosis result, identifying the type of skin condition detected. The design ensures accessibility and simplicity, making it usable by individuals without technical expertise. This module is crucial for enabling real-time diagnosis support, especially for users in rural or medically underserved locations, reducing dependency on physical consultations and offering instant insights into potential skin diseases.

Software Requirements

Programming Language: Python Development Environment: Anaconda, Visual Studio Code (VS Code) Libraries: NumPy and Pandas for data manipulation and processing Machine Learning Algorithms: SVM and Naïve Bayes classifiers Feature Extraction: Histogram of Oriented Gradients (HOG) These tools provide a robust foundation for implementing image processing and classification tasks efficiently. Python’s rich ecosystem ensures ease of model development and integration, while VS Code and Anaconda offer streamlined environments for debugging and execution.

Hardware Requirements

Processor: Minimum Intel Core i5 or equivalent RAM: 8 GB or more (for smooth performance during image processing) Storage: At least 256 GB of available disk space GPU (optional but recommended): NVIDIA GPU with CUDA support for faster model inference Operating System: Windows/Linux/Mac While the model can function on standard hardware, higher computational power enhances processing speed and real-time responsiveness. In absence of a GPU, model performance may degrade, especially during training or handling larger datasets.