Download Project Document/Synopsis
Every year, a large number of children worldwide die from pneumonia. In 2016, an estimated 1.2 million cases of pneumonia in children under the age of five were recorded, with 880,000 deaths.
Pneumonia is a respiratory infection caused by bacteria or viruses that affects many people, particularly in poor and underdeveloped countries with high levels of pollution, unsanitary living conditions, overcrowding, as well as inadequate medical infrastructure.
A chest X-ray test is a frequent and inexpensive diagnostic imaging method. Clinical diagnosis of the lung or chest X-ray might be in great demand. However, it is sometimes more difficult than lung diagnosis by computed tomography imaging for the chest.
Our Hybrid Pneumonia Detection System is developed for detecting lung diseases from X-ray images. Early detection of pneumonia is critical for curative therapy and increasing survival rates. The most often used approach for detecting pneumonia is chest X-ray imaging. However, examining chest X-rays is a difficult process that is vulnerable to subjective variability.
Here, for the hybrid model, DNN will be used with the Ada Boost classifier and for the dataset chest, x-ray images will be used. The front-end involves Html, CSS, and JavaScript and the back-end involves Python. The framework used is Django and the database is MySQL.
Advantages
- It’s easy to maintain.
- It’s user-friendly.
- No human intervention is required for marking attendance.
- The system recognizes faces and records attendance