Tuberculosis Detection
Sound-Based TB Detection: Harnessing Deep Learning for Early Diagnosis.
Created on 9th June 2024
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Tuberculosis Detection
Sound-Based TB Detection: Harnessing Deep Learning for Early Diagnosis.
The problem Tuberculosis Detection solves
The Tuberculosis Detection mobile application addresses several critical problems:
Early Detection:
Tuberculosis (TB) can be detected in its early stages, allowing for timely intervention and treatment, which can significantly reduce the spread of the disease.
Accessibility:
The application makes TB screening accessible to remote and underserved areas where medical facilities and healthcare professionals may be scarce. Mobile devices are more widely available than specialized medical equipment.
Cost-Effective:
Traditional TB testing methods can be expensive and require laboratory infrastructure. A mobile application provides a more cost-effective solution by leveraging existing mobile technology and deep learning algorithms.
Speed:
The application can provide rapid results, enabling quicker decision-making and reducing the waiting time for diagnosis, which is crucial for infectious disease control.
Consistency and Accuracy:
Deep learning models can provide consistent and accurate results, reducing the likelihood of human error in diagnosis.
Challenges we ran into
The Main Challenges faced are:
- Model Training Accuracy and Validation:
Challenge:
Developing a highly accurate deep learning model that can reliably detect TB from medical images.
Solution:
Using advanced neural network architectures such as Convolutional Neural Networks (CNNs) and Transfer Learning. Conducting extensive validation and testing using separate validation and test datasets.
- Handling Imbalanced Datasets:
Challenge:
An imbalanced distribution of TB-positive and TB-negative cough recordings.
Solution:
Applying data augmentation techniques such as pitch shifting, time stretching, and adding background noise to balance the dataset. This increases the diversity of TB-positive cough sounds and enhances the model’s generalization capabilities.
- Build Configuration Errors:
Challenge:
Incorrect build configurations in the build.gradle files.
Solution:
Review and correct the configurations in both android/build.gradle and android/app/build.gradle. Ensure that you have the correct minSdkVersion, targetSdkVersion, and other settings that match your project requirements. Also, make sure that all required repositories are specified in the build.gradle files.
Technologies used