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This final year project focuses on early pneumonia detection using chest X-ray images, a critical tool for timely treatment and saving lives, particularly among young children. The project compares Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) applied to a Kaggle dataset of chest X-rays. A user-friendly graphical user interface (GUI) is developed to accept X-ray inputs, predict pneumonia presence, and display congestion levels. CNNs outperformed MLPs, achieving high accuracy, and a custom CNN is implemented in the GUI. This IEEE-based project is ideal for computer science students seeking innovative machine learning solutions in healthcare.
Upon purchasing this project online, you will receive recorded video tutorials, comprehensive documentation, complete frontend (HTML, CSS, JavaScript) and backend (Python with CNN and MLP implementations) codes, reports, PPTs, and datasets, all sent automatically to your email. For further support, contact our technical team at +91 8088605682. Please note that once payment is made, no refunds will be issued under any circumstances.
Receive fully functional and tested code for pneumonia detection, implemented using Python with CNN and MLP algorithms, including a user-friendly GUI.
Get detailed documentation, including reports, PPTs, and datasets for research papers, sent automatically to your email upon purchase.
For personalized guidance on installation and implementation, contact our technical team at +91 8088605682 to arrange one-to-one online sessions (additional charges apply).
For project customization or additional feature integration, contact our technical team at +91 8088605682 to discuss your requirements (additional charges apply).
For hands-on, in-person assistance, contact our team at +91 8088605682 to arrange offline support at our Bangalore center (additional charges apply).
This is one of the best IEEE Machine Learning project ideas for final-year students. The project includes complete frontend (HTML, CSS, JavaScript) and backend (Python with CNN and MLP implementations) codes, along with detailed explanations to ensure thorough understanding. Smart AI Technologies offers thorough guidance, complete support in implementing the pneumonia detection system, and content for your report and IEEE paper publication, making it a commercially viable solution for applications in healthcare diagnostics and beyond.