Artificial Intelligence Based Disease Detection In Medical Images
Artificial intelligence market in the field of healthcare is growing quite rapidly worldwide. It is helping doctors in diagnosis and predicting the future of patient health. It will help us apply machine learning to concrete problems in medicine. Our project "Artificial Intelligence Based Disease De
2025-06-28 16:25:10 - Adil Khan
Artificial Intelligence Based Disease Detection In Medical Images
Project Area of Specialization Artificial IntelligenceProject SummaryArtificial intelligence market in the field of healthcare is growing quite rapidly worldwide. It is helping doctors in diagnosis and predicting the future of patient health. It will help us apply machine learning to concrete problems in medicine. Our project "Artificial Intelligence Based Disease Detection in Medical Images" works on the principle of neural networks for the detection of diseased and non-diseased X-RAYS. Neural networks are machine learning models, also called parallel distributed processing systems, with multiple layers between input and output. These types of machine learning models mimics the way humans learn. Different types of neural networks are popularly used today, whereas, in our project, CNN is used for the classification of medical images.
Medical image analysis has become an active and broad area of research due to its high clinical impact in recent decades. As you know that manual analysis of X-ray images is a tiring task and requires high professional skills. It also consumes a lot of time with chances of human error. Also the radiologists are overburden that causes a delay in disease diagnosis. So, there should be a system that automatically extracts information from medical images and requires no expert recognition. It also saves time and provides high accuracy results. Different algorithms have been developed for the classification of X-rays but there is still room to get even better results.
The main goal is to assist and facilitate the radiologists by early detection of diseases, and to achieve high diagnostic accuracy. The work is divided into two phases. Phase 1 is the training and testing of an online dataset using GPU server. Phase 2 is the training and testing of local dataset using a mobile APP.
Project ObjectivesThe main goal of the project is to assist and facilitate radiologists by classifying medical images
It also helps in;
Achieving high diagnostic accuracy
High sensitivity rate
Early detection of diseases
Reducing chances of human error
Saves time
Project Implementation MethodThe project implementation method includes two phases
Phase 1: Training Algorithms using online data sets
Hardware: GPU servers/Google colab
Software : Anaconda / Jupyter
Phase 2: Training local data set
Mobile app development
Assist and facilitate the radiologist
Less human error
Rapid diagnosis and immediate detection
Reduce reading time
Technical Details of Final DeliverableThe final deliverables include a network or a system that is used for classification of medical images. It also includes a mobile APP, The mobile APP takes pictures, sends them to the network that is placed on cloud, and as a result it gives the predicted output.
Final Deliverable of the Project HW/SW integrated systemCore Industry MedicalOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 59000 | |||
| External hard drive | Equipment | 1 | 10000 | 10000 |
| Smart device or phone | Equipment | 1 | 35000 | 35000 |
| Software subscriptions | Equipment | 2 | 2000 | 4000 |
| X-RAY images in hard form | Miscellaneous | 50 | 200 | 10000 |