filmov
tv
Deep Learning based Blood Group Detection using Fingerprint | Python Machine Learning IEEE Project

Показать описание
Deep Learning based Blood Group Detection using Fingerprint | Python Machine Learning Final Year IEEE Project 2024 - 2025.
(or)
To buy this project in ONLINE, Contact:
📌Our Proposed Project Title: Deep Learning based Blood Group Detection using Fingerprint.
💡Implementation: Python.
🔬Algorithm / Model Used: MobileNetV2 Architecture.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/.
📢OUR PROPOSED PROJECT ABSTRACT:
Blood group detection is a vital procedure in medical diagnostics, particularly for transfusion medicine, emergency healthcare, and personalized treatment plans. Current methods rely on the physical collection of blood samples and laboratory-based testing, which can be time-consuming and dependent on specialized resources. To address these limitations, this project proposes a novel deep learning-based system for blood group detection, leveraging two distinct methods: blood images and fingerprint images. Developed using Python for backend processing and Flask as the web framework, the system provides a user-friendly interface powered by HTML, CSS, and JavaScript, enabling efficient blood group detection in both traditional and non-invasive modes.
In the first mode, blood images are used to identify the blood group, utilizing the MobileNetV2 architecture model. The model was trained on a dataset of 750 blood images, with 500 images allocated for training and 250 for testing.
The second mode of detection introduces a groundbreaking non-invasive approach, where fingerprint images are used to predict the individual’s blood group. The MobileNetV2 architecture was also applied to this task, trained on a significantly larger dataset of 10,477 fingerprint images, with 6,000 used for training and 4,477 for testing.
📌IEEE Base paper Title: Artificial Intelligence and Image Processing Techniques for Blood Group Prediction.
📖REFERENCE:
Tannmay Gupta, “Artificial Intelligence and Image Processing Techniques for Blood Group Prediction”, 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), IEEE CONFERENCE, 2024.
#bloodgroup #python #aiproject #pythonprojects #machinelearningproject #pythonprogramming #pythonprojectforbeginners #pythonprojectideas #pythonmachinelearning #machinelearning #machinelearningpython #finalyearproject #ieeeprojects #finalyearprojects #datascience #datascienceproject #artificialintelligenceproject #projects #deeplearning #deeplearningproject #computerscienceproject #deeplearningprojects #majorprojects #academicprojects #majorproject #bloodgroups #deeplearning #ai #aritificialintelligence #fingerprint #imageprocessing #imageprocessingpython #ml #mlproject #mlprojects #aiprojects #beprojects #mcaprojects #engineeringprojects
(or)
To buy this project in ONLINE, Contact:
📌Our Proposed Project Title: Deep Learning based Blood Group Detection using Fingerprint.
💡Implementation: Python.
🔬Algorithm / Model Used: MobileNetV2 Architecture.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.5000/.
📢OUR PROPOSED PROJECT ABSTRACT:
Blood group detection is a vital procedure in medical diagnostics, particularly for transfusion medicine, emergency healthcare, and personalized treatment plans. Current methods rely on the physical collection of blood samples and laboratory-based testing, which can be time-consuming and dependent on specialized resources. To address these limitations, this project proposes a novel deep learning-based system for blood group detection, leveraging two distinct methods: blood images and fingerprint images. Developed using Python for backend processing and Flask as the web framework, the system provides a user-friendly interface powered by HTML, CSS, and JavaScript, enabling efficient blood group detection in both traditional and non-invasive modes.
In the first mode, blood images are used to identify the blood group, utilizing the MobileNetV2 architecture model. The model was trained on a dataset of 750 blood images, with 500 images allocated for training and 250 for testing.
The second mode of detection introduces a groundbreaking non-invasive approach, where fingerprint images are used to predict the individual’s blood group. The MobileNetV2 architecture was also applied to this task, trained on a significantly larger dataset of 10,477 fingerprint images, with 6,000 used for training and 4,477 for testing.
📌IEEE Base paper Title: Artificial Intelligence and Image Processing Techniques for Blood Group Prediction.
📖REFERENCE:
Tannmay Gupta, “Artificial Intelligence and Image Processing Techniques for Blood Group Prediction”, 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), IEEE CONFERENCE, 2024.
#bloodgroup #python #aiproject #pythonprojects #machinelearningproject #pythonprogramming #pythonprojectforbeginners #pythonprojectideas #pythonmachinelearning #machinelearning #machinelearningpython #finalyearproject #ieeeprojects #finalyearprojects #datascience #datascienceproject #artificialintelligenceproject #projects #deeplearning #deeplearningproject #computerscienceproject #deeplearningprojects #majorprojects #academicprojects #majorproject #bloodgroups #deeplearning #ai #aritificialintelligence #fingerprint #imageprocessing #imageprocessingpython #ml #mlproject #mlprojects #aiprojects #beprojects #mcaprojects #engineeringprojects
Комментарии