Project Abstract

A pattern analysis within the medical sector is the primary objective of this project. More specifically, the project will concentrate on the utilisation of X-ray images for the purpose of identifying and categorising items. The primary algorithm utilised in this work is known as Convolutional Neural Networks, or CNNs for short. Kaggle, a website that is well-known for its massive dataset collection, was the source from which the dataset was obtained after it was collected. Because of this, the researchers will have access to a vast variety of data that is both relevant and diverse. Both the process of medical diagnosis and the process of planned therapy require X-ray images, so it is essential that object detection be accurate and effective. Utilising Convolutional Neural Networks (CNNs), which are particularly effective at image processing, the purpose of this research is to bring about an improvement in the detection of objects in X-ray pictures. Machine learning techniques will accomplish this. The CNN model will be trained, the dataset will be preprocessed, and the effectiveness of the model will be evaluated as part of the study. Medical imaging technologies are expected to be improved as a result of the findings of this study, which will assist medical professionals in providing more accurate diagnoses of patients and providing them with more effective treatment.

Keywords: Object Recognition, Convolutional Neural Network, X-Ray Images

 Conference Details

Session: Presentation Stream 25 at Presentation Slot 2

Location: GH049 at Wednesday 8th 13:30 – 17:00

Markers: Megan Venn-Wycherley, Fernando Maestre Avila

Course: MSc Data Science, Masters PG

Future Plans: I’m looking for work