Viral Disease Classification using Deep Learning

📌 Overview

This project focuses on the classification of viral diseases using deep learning techniques applied to viral genome sequences. The goal is to build an intelligent model that can automatically identify and classify viruses into their respective families, which can assist in bioinformatics research, early detection, and disease control strategies.

🚀 Features

• Collected and verified unique viral genome sequences from 10 virus families.
• Implemented a Convolutional Neural Network (CNN) for feature extraction.
• Integrated with an Extreme Learning Machine (ELM) for fast and efficient classification.
• Achieved improved accuracy compared to traditional methods.
• Scalable and adaptable for additional viral families.

🧬 Tech Stack

• Python
• TensorFlow / Keras (for CNN implementation)
• scikit-learn (for ELM and preprocessing)
• BioPython (for sequence handling)
• NumPy & Pandas (for data processing)

📊 Results

• Achieved high accuracy in classifying viral genomes into 10 distinct families.
• Demonstrated the effectiveness of the CNN + ELM hybrid approach in genomic data classification.
• Results were represented using a confusion matrix for clear performance evaluation.