Nazia Hameed

PhD researcher

Faculty:Faculty of Science and Engineering

Location: Chelmsford

nazia.hameed@student.anglia.ac.uk

Research interests

Nazia is supervised by

Thesis title

An Intelligent Mobile-Based Diagnosis System for Prominent Skin Diseases

Thesis abstract

Early detection of skin diseases is very important as skin diseases are spreading vigorously among humans. With the advancement and increased usage of smart phones; smartphones enabled skin diseases detection systems are really demanding but currently very few real time skin diseases detection systems are available for general public and mostly available are the paid. In this research authors propose and develop an innovative low cost real time smart phone enabled intelligent health care system for the detection of skin diseases. Proposed system is developed by using the computer vision and image processing techniques. In pre-processing step image processing techniques are applied for image enhancement. Different features (morphological, color, texture and histogram) are extracted from the skin images in feature selection step and finally machine learning techniques like Support Vector Machine (SVM) and Artificial Neural Network (ANN) are applied on these features for disease diagnosis in classification step. E-Skin Scanner provides an automated and cost effective solution for the patients living in remote and developing areas and using this system they can scan and analyse their skin lesions and can make regular skin examinations.

Selected recent publications

Hameed, N., Shabut, A., & Hossain, M. A. “A Computer-aided diagnosis system for classifying prominent skin lesions using machine learning”, 10th IEEE Computer Science and Electronic Engineering Conference, University of Essex, 2018

Hameed, N., Shabut, A., & Hossain, M. A., “Multi-Class Multi-Level Classification Algorithm for Skin Lesion Classification using Advanced Machine Learning Techniques”, Expert Systems with applications (Submitted)

Hameed, N., Hassan, K.A. and Hossain, M.A. "A comprehensive survey on image-based computer aided diagnosis systems for skin cancer," 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), Chengdu, China, 2016, pp. 205-214. doi:10.1109/SKIMA.2016.7916221,

Taufiq, M.A., Hameed, N., Anjum, A., Hameed, F. (2017) m-Skin Doctor: A Mobile Enabled System for Early Melanoma Skin Cancer Detection Using Support Vector Machine. In: Giokas K., Bokor L., Hopfgartner F. (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer

Recent presentations and conferences

Posters

e-Skin Scanner: Smart Phone enabled Skin Diseases Detection System, presented in Faculty of Science & Technology 6th Annual Research Conference 2016, Anglia Ruskin University (Won 3 prize)

A Quality Assessment Framework for Smart Phone Based Skin Cancer Detection Apps presented in Faculty of Science & Technology 7th Annual Research Conference 2017. Anglia Ruskin University

Skin Disease Classification Using Machine Learning Techniques, presented in Twelfth Annual Research Student Conference 2018, Anglia Ruskin University

Presentations

Skin Disease Classification Using Machine Learning Techniques, presented in the Twelfth Annual Research Student Conference 2018

A computer-aided diagnosis system for classifying prominent skin lesions using machine learning presented in IEEE 10th Computer Science and Electronic Engineering Conference, 19th – 21st September 2018, University of Essex, Colchester

Multi-Class Multi-Level Classification Algorithm for Skin Disease Classification, presented in AI for Medical Informatics, Engineering & Devices Workshop, Anglia Ruskin University