Welcome to the Tuberculosis Detection Project! In this project, the aim is to develop a machine learning model to detect tuberculosis (TB) from chest X-ray images. The dataset consists of chest X-ray images from individuals diagnosed with TB and those without TB.
The dataset comprises chest X-ray images categorized into two classes: normal and tuberculosis cases. It includes a total of several thousand images, with labels indicating the presence or absence of TB.
The primary objective is to build a robust machine learning model capable of accurately identifying TB cases from chest X-ray images. Such a model could assist healthcare professionals in early diagnosis and treatment of tuberculosis, thereby potentially reducing its spread and improving patient outcomes.
The evaluation of the model is based on metrics such as accuracy, F1 score, and the confusion matrix. These metrics provide insights into the model's performance in correctly classifying TB and non-TB cases, as well as its ability to minimize false positives and false negatives.
This project addresses the critical task of tuberculosis detection using machine learning techniques applied to medical imaging data. By leveraging deep learning and image processing methods, the model aims to contribute to the improvement of TB diagnosis, particularly in resource-constrained settings. Let's work towards enhancing tuberculosis detection and healthcare outcomes together! 🩺🔍