Covid Detection using Machine Learning
COVID future prediction & detection is a desktop based application designed and developed using python programming language. It allows the user to predict the future data of the COVID after N days in X country. It uses Time series as a dataset & pre-trained Fbprophet model for predictions. Along with predictions, it can detect whether the person is infected by corona or not by scanning x-ray of chest of person.
COVID-19 DETECTION USING X-RAY imaging involves the analysis of chest X-ray images to identify potential signs of the disease. While the primary method of COVID-19 diagnosis is through molecular tests like polymerase chain reaction (PCR), X-ray imaging can be a useful supplementary tool in certain situations.
When a person is infected with COVID-19, the virus can cause pneumonia and other respiratory symptoms. These changes in the lungs can be visible on X-ray images, which can aid in the identification of COVID-19 cases. However, it’s important to note that X-rays cannot definitively confirm or rule out a COVID-19 infection on their own.
Radiologists or trained medical professionals examine the X-ray images for specific patterns and abnormalities that might suggest COVID-19 pneumonia. Some common findings include bilateral (both sides of the lungs) patchy infiltrates, ground-glass opacities, and consolidations. These abnormalities may differ from typical pneumonia or other lung diseases, providing clues that COVID-19 could be present.
Covid Disease Detection from Chest X-Ray using Python
COVID-19 DETECTION USING X-RAY IN PYTHON involves using machine learning techniques to classify X-ray images as either COVID-19 positive or negative. The goal is to build a model that can learn to identify patterns or abnormalities specific to COVID-19 pneumonia in the lung X-rays.
Here are the steps for COVID DETECTION USING X-RAY IN PYTHON:
1.Data Collection: Gather a dataset of chest X-ray images, labeled as either COVID-19 positive or negative.
2.Data Preprocessing: Resize the images to a uniform size and normalize the pixel values to a range between 0 and 1. Apply data augmentation techniques if needed to increase the dataset’s diversity.
3.Model Selection: Choose a pre-trained deep learning model suitable for image classification. For example, VGG16, ResNet, or DenseNet are common choices.
4.Model Building: Remove the top classification layers from the chosen pre-trained model and add new layers for binary classification (COVID-19 positive or negative).
5.Transfer Learning: Optionally, freeze the weights of the pre-trained layers to avoid overfitting and speed up training. Train only the newly added layers.
6.Model Training: Train the model on the preprocessed X-ray images and their corresponding labels. Use an appropriate optimizer and loss function for binary classification.
7.Model Evaluation: Assess the model’s performance on a separate test set using evaluation metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
8.Fine-tuning (Optional): If the model’s performance is not satisfactory, fine-tune the hyperparameters or unfreeze some of the pre-trained layers for further training.
9.Deployment: Deploy the trained model to predict COVID-19 cases in real-world scenarios. It’s crucial to emphasize that the model is a screening tool and should be used alongside clinical evaluation.
10.Continuous Improvement: Continuously monitor the model’s performance and update it with new data to improve its accuracy and generalizability over time.
CONCLUSION :-
COVID DETECTION USING X-RAY IN PYTHON models can help identify potential COVID-19 cases from X-ray images, enabling early detection of the disease and timely intervention to reduce transmission and severity.
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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