A technical master’s thesis in Computer Technologies Engineering, tagged Radiology Based Classification of Osteoarthritis of “Knee Joints”, was discussed at the Engineering Technical College / Mosul. The thesis presented by the student (Hudhaifa Amer Ahmed Khader) examined the use of deep learning techniques based on the Convolutional Neural Network to classify osteoarthritis in the knee joint from x-Ray according to the severity of the injury through the use of (VGG16-19, ResNet50, Efficient NetB7 with (Fine Tune) work for her.

The thesis indicated:

1. GUI design for the purpose of making it easier to use and test the model on new data.
2. Create a smartphone app using Android studio for the purpose of x-Ray classification according to the severity of the infection.

The goal of the thesis:

1. Building and implementing a classification system for osteoarthritis of the knee joint using Raspberry Pi4
2. Reducing the burden on doctors and assisting the radiologist in assessing the severity of the disease and enabling them to make the optimal diagnosis and prescribe the correct treatment.
3. Reduce diagnosis time and no need for a high specification computer to perform the task.

The thesis recommended the possibility of developing the proposed model by using more medical images such as CT-scan, and MRI to