View Proposal

---

Proposer

Marta Vallejo

Title

Approaching a multi-label classification problem for the diagnosis of ear diseases using machine learning techniques

Goal

Implementing state-of-the-art techniques for concurrent multiple diseases detection from ears’ otoscopes.

Description

Traditional methods of diagnosing ear diseases often involve manual interpretation of clinical symptoms, which can lead to subjective results and delays in accurate treatment. This project seeks to develop a robust and accurate diagnostic tool that can analyse a range of ear-related symptoms to identify and classify people suffering from various ear diseases at the same time. The project uses a medical image dataset collected in Chile to train advanced machine learning models to recognise patterns indicative of different ear conditions, including otitis media, otitis externa, and so on. The success of this project has the potential to lead to a journal publication by providing results that improve patient outcomes, reduce misdiagnoses, and provide a reliable and accessible tool for healthcare professionals. This project is in collaboration with Dr Fernando Auat (Harper University).

Resources

A dataset of video recordings will be provided.

Background

Machine learning, deep learning, medical imagen

Url

Difficulty Level

High

Ethical Approval

Full

Number Of Students

1

Supervisor

Marta Vallejo

Keywords

machine learning, deep learning, medical imagen

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Bachelor of Science in Statistical Data Science