- Lecturer | Researcher
- M.Phil (Computer Sciences)
- Phd-Bioinformatics (In Progress)
- Marie Sklodowska-Curie Fellow
M.Phil Computer Sciences (18 Years of Education) with Professional Experience as Lecturer, Lab Instructor & First Stage Researcher. Strong Communication Skills & Reliable Practical Knowledge of Research Work Management, in Multi-Disciplinary Projects.
Looking Forward to take on Challenging & Innovative Research Projects.
- Cancer Genomics
- Image Processing
- Machine Learning
- Medical Image Analysis
Early Stage Researcher
Research Work Management
Multi-Disciplinary Projects Exposure
Lecturer & Lab Instructor
Strong Communication Skills
Content Writer | Blogger
Lung Segmentation in CT Scans
for Lung Nodule and Cancer Detection
- Authors : Noor Kherreh, Saira Bilal, Muhammad Shahid Farid
- Conference : Conference on Microbiology and Molecular Genetics (MMG 2018), pp. 161
- Date : Feb 2018
- Status : Published
Lung Cancer Detection using CT Scan Image
- Authors : Noor Kherreh, Muhammad Shahid Farid, Saira Bilal
- Journal : International Journal of Medical Informatics
- Date : May 2018.
- Status : Under Review
2012 – 2015
2016 – 2018
2016 – 2018
Stage & Grade prediction of Breast Cancer
using Machine Learning
Breast cancer has high incident rate, there has been an alarming increase in the number of patients. Cancer is a very complex disease, it has many types,stages and degrees. Due to its heterogeneous nature, the treatment vary from patient to patient. Thus it is very essential to diagnose the stage of cancer and the degree of tumor for more effective treatment. Machine learning algorithms have many applications in research field of cancer.In this work we have applied different machine learning algorithms on the data set of Pathological reports collected from local hospitals. The developed system predicts the stage and the degree of cancer by taking features such as Tumor Size, Nodules Affected,Tubular Differentiation,Pleomorphism,Mitotic Rate etc.
Applications of Machine Learning
in Cancer Prediction
Machine learning has lot of applications in cancer research work. Advent of gene expression data has opened new ways of application of machine learning algorithms. Extensive research has used gene data but also other type of data such as pathological reports etc for prediction. Machine learning algorithms has two main classes supervised learning and unsupervised learning. Cancer researchers have mostly used supervised learning techniques as their accuracy is higher than unsupervised learning algorithm.
Research papers studied in this survey has a 5:1 of supervised to unsupervised learning algorithms. Only 14.28% of research papers have used unsupervised learning algorithms.
- Email : email@example.com | firstname.lastname@example.org
- Location : Galway, Ireland | Lahore, Pakistan