Medical Image Classification Repository
This repository contains three Jupyter Notebook files that demonstrate the classification of medical images for various tasks. The notebooks utilize state-of-the-art deep learning models and techniques to classify different medical conditions and provide insights into COVID-19 cases and colon cancer.
CovidMobileNetMulti.ipynb
COVID-19 Diagnosis
The CovidMobileNetMulti.ipynb notebook showcases the implementation of a mobile net architecture using the fastai library. It focuses on the diagnosis of pneumonia, normal cases, and COVID-19 based on chest X-ray images. The notebook demonstrates the application of deep learning for automated classification of COVID-19 cases, aiding in effective triaging and patient management.
CovidPunjabDataVisualization.ipynb
COVID-19 Data Visualization
The CovidPunjabDataVisualization.ipynb notebook employs Geopandas and Pandas libraries to perform exploratory data analysis (EDA) on COVID-19 cases in Punjab. It provides insightful visualizations and graphs to gain a better understanding of the COVID-19 situation in the region. The notebook assists in visualizing the spread of the virus, identifying hotspots, and tracking the progress of the pandemic.
LC25000FastAI18Colon.ipynb
Colon Cancer Classification
The LC25000FastAI18Colon.ipynb notebook leverages the power of the FastAI library and the ResNet18 model to classify histopathological images of colon tissue into normal and cancerous categories. By automating the classification process, this notebook aids in the early detection and diagnosis of colon cancer, potentially saving lives through timely intervention.
Feel free to explore these notebooks to gain insights into medical image classification and COVID-19 analysis. They serve as valuable resources for researchers, practitioners, and enthusiasts looking to leverage deep learning techniques for medical imaging tasks.